diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/benchmark_results.html b/benchmark_results.html new file mode 100644 index 0000000..e5d5155 --- /dev/null +++ b/benchmark_results.html @@ -0,0 +1,733 @@ + + + + + + + + + + +mms_benchmark - Benchmark results + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Benchmark results

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+ The results of our benchmark for several Language Models using data from MMS. +
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Our preliminary results has been presented in (Rajda et al. 2022) and finally presented in (Augustyniak et al. 2023) review at NeurIPS’23.

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Benchmark results - F1 Macro scores

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Models

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ModelInf. time [s]#params#langsbasedatareference
mT51.69277M101T5\(CC^b\)(Xue et al. 2021)
LASER1.6452M93BiLSTM\(OPUS^c\)(Artetxe and Schwenk 2019)
mBERT1.49177M104BERTWiki(Devlin et al. 2019)
MPNet**1.38278M53XLM-R\(OPUS^c\), \(MUSE^d\), \(Wikititles^e\)(Reimers and Gurevych 2020)
XLM-R-dist**1.37278M53XLM-R\(OPUS^c\), \(MUSE^d\), \(Wikititles^e\)(Reimers and Gurevych 2020)
XLM-R1.37278M100XLM-RCC(Conneau et al. 2020)
LaBSE1.36470M109BERTCC, Wiki + mined bitexts(Feng et al. 2020)
DistilmBERT0.79134M104BERTWiki(Sanh et al. 2020)
mUSE-dist**0.79134M53DistilmBERT\(OPUS^c\), \(MUSE^d\), \(Wikititles^e\)(Reimers and Gurevych 2020)
mUSE-transformer*0.6585M16transformermined QA + bitexts, SNLI(Yang et al. 2020)
mUSE-cnn*0.1268M16CNNmined QA + bitexts, SNLI(Yang et al. 2020)
+
    +
  • * mUSE models were used in TensorFlow implementation in contrast to others in torch
    • +
  • a Base model is either monolingual version on which it was based or another multilingual model which was used and adopted
    • +
  • b Colossal Clean Crawled Corpus in multilingual version (mC4)
    • +
  • c multiple datasets from OPUS website (https://opus.nlpl.eu)
    • +
  • d bilingual dictionaries from MUSE (https://github.com/facebookresearch/MUSE)
    • +
  • e just titles from wiki articles in multiple languages
    • +
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Results

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References

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+Artetxe, Mikel, and Holger Schwenk. 2019. “Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond.” Transactions of the Association for Computational Linguistics 7 (September): 597–610. https://doi.org/10.1162/tacl_a_00288. +
+
+Augustyniak, Łukasz, Szymon Woźniak, Marcin Gruza, Piotr Gramacki, Krzysztof Rajda, Mikołaj Morzy, and Tomasz Kajdanowicz. 2023. “Massively Multilingual Corpus of Sentiment Datasets and Multi-Faceted Sentiment Classification Benchmark.” https://arxiv.org/abs/2306.07902. +
+
+Conneau, Alexis, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. “Unsupervised Cross-Lingual Representation Learning at Scale.” In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 8440–51. Online: Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.747. +
+
+Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–86. Minneapolis, Minnesota: Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1423. +
+
+Feng, Fangxiaoyu, Yinfei Yang, Daniel Cer, Naveen Arivazhagan, and Wei Wang. 2020. “Language-agnostic BERT Sentence Embedding.” Computing Research Repository arXiv:2007.01852. https://arxiv.org/abs/2007.01852. +
+
+Rajda, Krzysztof, Lukasz Augustyniak, Piotr Gramacki, Marcin Gruza, Szymon Woźniak, and Tomasz Kajdanowicz. 2022. “Assessment of Massively Multilingual Sentiment Classifiers.” In Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, 125–40. Dublin, Ireland: Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.wassa-1.13. +
+
+Reimers, Nils, and Iryna Gurevych. 2020. “Making Monolingual Sentence Embeddings Multilingual Using Knowledge Distillation.” In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 4512–25. Online: Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.365. +
+
+Sanh, Victor, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2020. “DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.” Computing Research Repository arXiv:1910.01108. https://arxiv.org/abs/1910.01108. +
+
+Xue, Linting, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021. “MT5: A Massively Multilingual Pre-Trained Text-to-Text Transformer.” In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 483–98. Online: Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.naacl-main.41. +
+
+Yang, Yinfei, Daniel Cer, Amin Ahmad, Mandy Guo, Jax Law, Noah Constant, Gustavo Hernandez Abrego, et al. 2020. “Multilingual Universal Sentence Encoder for Semantic Retrieval.” In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 87–94. Online: Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-demos.12. +
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+ + + + \ No newline at end of file diff --git a/citations.html b/citations.html new file mode 100644 index 0000000..70ae343 --- /dev/null +++ b/citations.html @@ -0,0 +1,3063 @@ + + + + + + + + + + +mms_benchmark - MMS Dataset Citations + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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MMS Dataset Citations

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+ Citations for the MMS datasets +
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+

Citations

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+
+
+

Dataset id: ar_arsentdl

+
    +
  • Domain: social_media
    • +
  • Language: ar
    • +
  • Language family: Afro-Asiatic
    • +
  • Genus: Semitic
    • +
  • Definite articles: definite affix
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 3
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative intonation only
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: weakly suffixing
    • +
  • Coding of nominal plurality: mixed morphological plural
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@InProceedings{dataset_ar_arsentdl,
+    author = {Ramy Baly and
+                Alaa Khaddaj and
+                Hazem M. Hajj and
+                Wassim El{-}Hajj and
+                Khaled Bashir Shaban},
+    title = {{ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets}},
+    booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
+    year = {2018},
+    month = {may},
+    date = {7-12},
+    location = {Miyazaki, Japan},
+    editor = {Hend Al-Khalifa and King Saud University and KSA Walid Magdy and University of Edinburgh and UK Kareem Darwish and Qatar Computing Research Institute and Qatar Tamer Elsayed and Qatar University and Qatar},
+    publisher = {European Language Resources Association (ELRA)},
+    address = {Paris, France},
+    isbn = {979-10-95546-25-2},
+    language = {english}
+}
+
+
+
+

Dataset id: ar_astd

+
    +
  • Domain: social_media
    • +
  • Language: ar
    • +
  • Language family: Afro-Asiatic
    • +
  • Genus: Semitic
    • +
  • Definite articles: definite affix
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 3
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative intonation only
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: weakly suffixing
    • +
  • Coding of nominal plurality: mixed morphological plural
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@inproceedings{dataset_ar_astd,
+    title = "{ASTD}: {A}rabic Sentiment Tweets Dataset",
+    author = "Nabil, Mahmoud  and
+        Aly, Mohamed  and
+        Atiya, Amir",
+    booktitle = "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
+    month = sep,
+    year = "2015",
+    address = "Lisbon, Portugal",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/D15-1299",
+    doi = "10.18653/v1/D15-1299",
+    pages = "2515--2519",
+}
+
+
+
+

Dataset id: ar_bbn

+
    +
  • Domain: social_media
    • +
  • Language: ar
    • +
  • Language family: Afro-Asiatic
    • +
  • Genus: Semitic
    • +
  • Definite articles: definite affix
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 3
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative intonation only
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: weakly suffixing
    • +
  • Coding of nominal plurality: mixed morphological plural
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@inproceedings{dataset_ar_bbn,
+    title = "Sentiment after Translation: A Case-Study on {A}rabic Social Media Posts",
+    author = "Salameh, Mohammad  and
+        Mohammad, Saif  and
+        Kiritchenko, Svetlana",
+    booktitle = "Proceedings of the 2015 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
+    month = may # "{--}" # jun,
+    year = "2015",
+    address = "Denver, Colorado",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/N15-1078",
+    doi = "10.3115/v1/N15-1078",
+    pages = "767--777",
+}
+
+
+
+

Dataset id: ar_brad

+
    +
  • Domain: reviews
    • +
  • Language: ar
    • +
  • Language family: Afro-Asiatic
    • +
  • Genus: Semitic
    • +
  • Definite articles: definite affix
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 3
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative intonation only
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: weakly suffixing
    • +
  • Coding of nominal plurality: mixed morphological plural
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@INPROCEEDINGS{dataset_ar_brad,
+    author={Elnagar, Ashraf and Einea, Omar},
+    booktitle={2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)}, 
+    title={{BRAD} 1.0: Book reviews in Arabic dataset}, 
+    year={2016},
+    volume={},
+    number={},
+    pages={1-8},
+    doi={10.1109/AICCSA.2016.7945800}
+}
+
+
+
+

Dataset id: ar_hard

+
    +
  • Domain: reviews
    • +
  • Language: ar
    • +
  • Language family: Afro-Asiatic
    • +
  • Genus: Semitic
    • +
  • Definite articles: definite affix
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 3
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative intonation only
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: weakly suffixing
    • +
  • Coding of nominal plurality: mixed morphological plural
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@Book{dataset_ar_hard,
+    author="Elnagar, Ashraf
+    and Khalifa, Yasmin S.
+    and Einea, Anas",
+    title={Hotel Arabic-Reviews Dataset Construction for Sentiment Analysis Applications},
+    bookTitle="Intelligent Natural Language Processing: Trends and Applications",
+    year="2018",
+    publisher="Springer International Publishing",
+    address="Cham",
+    pages="35--52",
+    isbn="978-3-319-67056-0",
+    doi="10.1007/978-3-319-67056-0_3",
+    url="https://doi.org/10.1007/978-3-319-67056-0_3"
+}
+
+
+
+

Dataset id: ar_labr

+
    +
  • Domain: reviews
    • +
  • Language: ar
    • +
  • Language family: Afro-Asiatic
    • +
  • Genus: Semitic
    • +
  • Definite articles: definite affix
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 3
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative intonation only
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: weakly suffixing
    • +
  • Coding of nominal plurality: mixed morphological plural
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@inproceedings{dataset_ar_labr,
+    title = "{LABR}: A Large Scale {A}rabic Book Reviews Dataset",
+    author = "Aly, Mohamed  and
+        Atiya, Amir",
+    booktitle = "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
+    month = aug,
+    year = "2013",
+    address = "Sofia, Bulgaria",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/P13-2088",
+    pages = "494--498",
+}
+
+
+
+

Dataset id: ar_oclar

+
    +
  • Domain: reviews
    • +
  • Language: ar
    • +
  • Language family: Afro-Asiatic
    • +
  • Genus: Semitic
    • +
  • Definite articles: definite affix
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 3
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative intonation only
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: weakly suffixing
    • +
  • Coding of nominal plurality: mixed morphological plural
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@inproceedings{dataset_ar_oclar,
+    author={Al Omari, Marwan and Al-Hajj, Moustafa and Hammami, Nacereddine and Sabra, Amani},
+    booktitle={2019 International Conference on Computer and Information Sciences (ICCIS)}, 
+    title={Sentiment Classifier: Logistic Regression for Arabic Services’ Reviews in Lebanon}, 
+    year={2019},
+    volume={},
+    number={},
+    pages={1-5},
+    doi={10.1109/ICCISci.2019.8716394}
+}
+
+
+
+

Dataset id: ar_semeval_2017

+
    +
  • Domain: mixed
    • +
  • Language: ar
    • +
  • Language family: Afro-Asiatic
    • +
  • Genus: Semitic
    • +
  • Definite articles: definite affix
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 3
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative intonation only
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: weakly suffixing
    • +
  • Coding of nominal plurality: mixed morphological plural
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@inproceedings{dataset_semeval_2017,
+    title = "{S}em{E}val-2017 Task 4: Sentiment Analysis in {T}witter",
+    author = "Rosenthal, Sara  and
+        Farra, Noura  and
+        Nakov, Preslav",
+    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
+    month = aug,
+    year = "2017",
+    address = "Vancouver, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/S17-2088",
+    doi = "10.18653/v1/S17-2088",
+    pages = "502--518",
+    abstract = "This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.",
+}
+
+
+
+

Dataset id: ar_syria_corpus

+
    +
  • Domain: social_media
    • +
  • Language: ar
    • +
  • Language family: Afro-Asiatic
    • +
  • Genus: Semitic
    • +
  • Definite articles: definite affix
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 3
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative intonation only
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: weakly suffixing
    • +
  • Coding of nominal plurality: mixed morphological plural
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@inproceedings{dataset_ar_bbn,
+    title = "Sentiment after Translation: A Case-Study on {A}rabic Social Media Posts",
+    author = "Salameh, Mohammad  and
+        Mohammad, Saif  and
+        Kiritchenko, Svetlana",
+    booktitle = "Proceedings of the 2015 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies",
+    month = may # "{--}" # jun,
+    year = "2015",
+    address = "Denver, Colorado",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/N15-1078",
+    doi = "10.3115/v1/N15-1078",
+    pages = "767--777",
+}
+
+
+
+

Dataset id: bg_twitter_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: bg
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@article{dataset_twitter_sentiment,
+    doi = {10.1371/journal.pone.0155036},
+    author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},
+    journal = {PLOS ONE},
+    publisher = {Public Library of Science},
+    title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},
+    year = {2016},
+    month = {05},
+    volume = {11},
+    url = {https://doi.org/10.1371/journal.pone.0155036},
+    pages = {1-26},
+    number = {5},
+}
+
+
+
+

Dataset id: bs_twitter_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: bs
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 5
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: other
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@article{dataset_twitter_sentiment,
+    doi = {10.1371/journal.pone.0155036},
+    author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},
+    journal = {PLOS ONE},
+    publisher = {Public Library of Science},
+    title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},
+    year = {2016},
+    month = {05},
+    volume = {11},
+    url = {https://doi.org/10.1371/journal.pone.0155036},
+    pages = {1-26},
+    number = {5},
+}
+
+
+
+

Dataset id: cs_facebook

+
    +
  • Domain: social_media
    • +
  • Language: cs
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 6-7
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative affix
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: MorphNeg
    • +
  • Prefixing vs suffixing: weakly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@inproceedings{dataset_cs_social_media,
+    title = "Sentiment Analysis in {C}zech Social Media Using Supervised Machine Learning",
+    author = "Habernal, Ivan  and
+      Pt{\'a}{\v{c}}ek, Tom{\'a}{\v{s}}  and
+      Steinberger, Josef",
+    booktitle = "Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
+    month = jun,
+    year = "2013",
+    address = "Atlanta, Georgia",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W13-1609",
+    pages = "65--74",
+}
+
+
+
+

Dataset id: cs_mall_product_reviews

+
    +
  • Domain: reviews
    • +
  • Language: cs
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 6-7
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative affix
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: MorphNeg
    • +
  • Prefixing vs suffixing: weakly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@inproceedings{dataset_cs_social_media,
+    title = "Sentiment Analysis in {C}zech Social Media Using Supervised Machine Learning",
+    author = "Habernal, Ivan  and
+      Pt{\'a}{\v{c}}ek, Tom{\'a}{\v{s}}  and
+      Steinberger, Josef",
+    booktitle = "Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
+    month = jun,
+    year = "2013",
+    address = "Atlanta, Georgia",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W13-1609",
+    pages = "65--74",
+}
+
+
+
+

Dataset id: cs_movie_reviews

+
    +
  • Domain: reviews
    • +
  • Language: cs
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 6-7
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative affix
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: MorphNeg
    • +
  • Prefixing vs suffixing: weakly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@inproceedings{dataset_cs_social_media,
+    title = "Sentiment Analysis in {C}zech Social Media Using Supervised Machine Learning",
+    author = "Habernal, Ivan  and
+      Pt{\'a}{\v{c}}ek, Tom{\'a}{\v{s}}  and
+      Steinberger, Josef",
+    booktitle = "Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
+    month = jun,
+    year = "2013",
+    address = "Atlanta, Georgia",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W13-1609",
+    pages = "65--74",
+}
+
+
+
+

Dataset id: cs_news_stance

+
    +
  • Domain: social_media
    • +
  • Language: cs
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 6-7
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative affix
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: MorphNeg
    • +
  • Prefixing vs suffixing: weakly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@inproceedings{dataset_cs_social_media,
+    title = "Sentiment Analysis in {C}zech Social Media Using Supervised Machine Learning",
+    author = "Habernal, Ivan  and
+      Pt{\'a}{\v{c}}ek, Tom{\'a}{\v{s}}  and
+      Steinberger, Josef",
+    booktitle = "Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
+    month = jun,
+    year = "2013",
+    address = "Atlanta, Georgia",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W13-1609",
+    pages = "65--74",
+}
+
+
+
+

Dataset id: de_dai_labor

+
    +
  • Domain: social_media
    • +
  • Language: de
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: 4
    • +
  • Order of subject, object, verb: no dominant order
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: more than one position
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@inproceedings{dataset_dai_labor,
+    author = {Narr, Sascha  and Michael Hülfenhaus and  Albayrak, Sahin},
+    title = {Language-Independent Twitter Sentiment Analysis},
+    booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},
+    year = {2012},
+    location = {Dortmund, Germany},
+}
+
+
+
+

Dataset id: de_ifeel

+
    +
  • Domain: social_media
    • +
  • Language: de
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: 4
    • +
  • Order of subject, object, verb: no dominant order
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: more than one position
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@inproceedings{dataset_dai_labor,
+    author = {Narr, Sascha  and Michael Hülfenhaus and  Albayrak, Sahin},
+    title = {Language-Independent Twitter Sentiment Analysis},
+    booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},
+    year = {2012},
+    location = {Dortmund, Germany},
+}
+
+
+
+

Dataset id: de_multilan_amazon

+
    +
  • Domain: reviews
    • +
  • Language: de
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: 4
    • +
  • Order of subject, object, verb: no dominant order
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: more than one position
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@inproceedings{dataset_multilan_amazon,
+    title = "The Multilingual {A}mazon Reviews Corpus",
+    author = {Keung, Phillip  and
+        Lu, Yichao  and
+        Szarvas, Gy{\"o}rgy  and
+        Smith, Noah A.},
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.emnlp-main.369",
+    doi = "10.18653/v1/2020.emnlp-main.369",
+    pages = "4563--4568",
+}
+
+
+
+

Dataset id: de_omp

+
    +
  • Domain: social_media
    • +
  • Language: de
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: 4
    • +
  • Order of subject, object, verb: no dominant order
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: more than one position
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@inproceedings{dataset_de_omp,
+    title = "Academic-Industrial Perspective on the Development and Deployment of a Moderation System for a Newspaper Website",
+    author = "Schabus, Dietmar  and
+        Skowron, Marcin",
+    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
+    month = may,
+    year = "2018",
+    address = "Miyazaki, Japan",
+    publisher = "European Language Resources Association (ELRA)",
+    url = "https://aclanthology.org/L18-1253",
+}
+
+
+
+

Dataset id: de_sb10k

+
    +
  • Domain: social_media
    • +
  • Language: de
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: 4
    • +
  • Order of subject, object, verb: no dominant order
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: more than one position
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@inproceedings{dataset_de_sb10k,
+    title = "A {T}witter Corpus and Benchmark Resources for {G}erman Sentiment Analysis",
+    author = "Cieliebak, Mark  and
+        Deriu, Jan Milan  and
+        Egger, Dominic  and
+        Uzdilli, Fatih",
+    booktitle = "Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media",
+    month = apr,
+    year = "2017",
+    address = "Valencia, Spain",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W17-1106",
+    doi = "10.18653/v1/W17-1106",
+    pages = "45--51",
+    abstract = "In this paper we present SB10k, a new corpus for sentiment analysis with approx. 10,000 German tweets. We use this new corpus and two existing corpora to provide state-of-the-art benchmarks for sentiment analysis in German: we implemented a CNN (based on the winning system of SemEval-2016) and a feature-based SVM and compare their performance on all three corpora. For the CNN, we also created German word embeddings trained on 300M tweets. These word embeddings were then optimized for sentiment analysis using distant-supervised learning. The new corpus, the German word embeddings (plain and optimized), and source code to re-run the benchmarks are publicly available.",
+}
+
+
+
+

Dataset id: de_twitter_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: de
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: 4
    • +
  • Order of subject, object, verb: no dominant order
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: more than one position
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@article{dataset_twitter_sentiment,
+    doi = {10.1371/journal.pone.0155036},
+    author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},
+    journal = {PLOS ONE},
+    publisher = {Public Library of Science},
+    title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},
+    year = {2016},
+    month = {05},
+    volume = {11},
+    url = {https://doi.org/10.1371/journal.pone.0155036},
+    pages = {1-26},
+    number = {5},
+}
+
+
+
+

Dataset id: en_amazon

+
    +
  • Domain: reviews
    • +
  • Language: en
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@inproceedings{dataset_en_amazon,
+    title = "Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects",
+    author = "Ni, Jianmo  and
+        Li, Jiacheng  and
+        McAuley, Julian",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
+    month = nov,
+    year = "2019",
+    address = "Hong Kong, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/D19-1018",
+    doi = "10.18653/v1/D19-1018",
+    pages = "188--197",
+}
+
+
+
+

Dataset id: en_dai_labor

+
    +
  • Domain: social_media
    • +
  • Language: en
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@inproceedings{dataset_dai_labor,
+    author = {Narr, Sascha  and Michael Hülfenhaus and  Albayrak, Sahin},
+    title = {Language-Independent Twitter Sentiment Analysis},
+    booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},
+    year = {2012},
+    location = {Dortmund, Germany},
+}
+
+
+
+

Dataset id: en_financial_phrasebank_sentences_75agree

+
    +
  • Domain: news
    • +
  • Language: en
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@article{dataset_en_financial_phrasebank_sentences_75agree,
+    author = {Malo, Pekka and Sinha, Ankur and Korhonen, Pekka and Wallenius, Jyrki and Takala, Pyry},
+    title = {Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts},
+    year = {2014},
+    issue_date = {April 2014},
+    publisher = {John Wiley & Sons, Inc.},
+    address = {USA},
+    volume = {65},
+    number = {4},
+    issn = {2330-1635},
+    url = {https://doi.org/10.1002/asi.23062},
+    doi = {10.1002/asi.23062},
+    journal = {Journal of the Association for Information Science and Technology},
+    month = {apr},
+    pages = {782–796},
+    numpages = {15},
+    keywords = {economics, automatic classification, linguistic analysis}
+}
+
+
+
+

Dataset id: en_multilan_amazon

+
    +
  • Domain: reviews
    • +
  • Language: en
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@inproceedings{dataset_multilan_amazon,
+    title = "The Multilingual {A}mazon Reviews Corpus",
+    author = {Keung, Phillip  and
+        Lu, Yichao  and
+        Szarvas, Gy{\"o}rgy  and
+        Smith, Noah A.},
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.emnlp-main.369",
+    doi = "10.18653/v1/2020.emnlp-main.369",
+    pages = "4563--4568",
+}
+
+
+
+

Dataset id: en_per_sent

+
    +
  • Domain: news
    • +
  • Language: en
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@inproceedings{dataset_en_per_sent,
+    title = "Author{'}s Sentiment Prediction",
+    author = "Bastan, Mohaddeseh  and
+        Koupaee, Mahnaz  and
+        Son, Youngseo  and
+        Sicoli, Richard  and
+        Balasubramanian, Niranjan",
+    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
+    month = dec,
+    year = "2020",
+    address = "Barcelona, Spain (Online)",
+    publisher = "International Committee on Computational Linguistics",
+    url = "https://aclanthology.org/2020.coling-main.52",
+    doi = "10.18653/v1/2020.coling-main.52",
+    pages = "604--615",
+}
+
+
+
+

Dataset id: en_poem_sentiment

+
    +
  • Domain: poems
    • +
  • Language: en
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@inproceedings{dataset_en_poem_sentiment,
+    title = "Investigating Societal Biases in a Poetry Composition System",
+    author = "Sheng, Emily  and
+        Uthus, David",
+    booktitle = "Proceedings of the Second Workshop on Gender Bias in Natural Language Processing",
+    month = dec,
+    year = "2020",
+    address = "Barcelona, Spain (Online)",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.gebnlp-1.9",
+    pages = "93--106",
+}
+
+
+
+

Dataset id: en_semeval_2017

+
    +
  • Domain: mixed
    • +
  • Language: en
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@inproceedings{dataset_semeval_2017,
+    title = "{S}em{E}val-2017 Task 4: Sentiment Analysis in {T}witter",
+    author = "Rosenthal, Sara  and
+        Farra, Noura  and
+        Nakov, Preslav",
+    booktitle = "Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)",
+    month = aug,
+    year = "2017",
+    address = "Vancouver, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/S17-2088",
+    doi = "10.18653/v1/S17-2088",
+    pages = "502--518",
+    abstract = "This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.",
+}
+
+
+
+

Dataset id: en_sentistrength

+
    +
  • Domain: social_media
    • +
  • Language: en
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@article{dataset_en_sentistrength,
+    author = {Thelwall, Mike and Buckley, Kevan and Paltoglou, Georgios},
+    title = {Sentiment Strength Detection for the Social Web},
+    year = {2012},
+    issue_date = {January 2012},
+    publisher = {John Wiley \& Sons, Inc.},
+    address = {USA},
+    volume = {63},
+    number = {1},
+    issn = {1532-2882},
+    url = {https://doi.org/10.1002/asi.21662},
+    doi = {10.1002/asi.21662},
+    abstract = {Sentiment analysis is concerned with the automatic extraction of sentiment-related
+    information from text. Although most sentiment analysis addresses commercial tass,
+    such as extracting opinions from product reviews, there is increasing interest in
+    the affective dimension of the social web, and Twitter in particular. Most sentiment
+    analysis algorithms are not ideally suited to this task because they exploit indirect
+    indicators of sentiment that can reflect genre or topic instead. Hence, such algorithms
+    used to process social web texts can identify spurious sentiment patterns caused by
+    topics rather than affective phenomena. This article assesses an improved version
+    of the algorithm SentiStrength for sentiment strength detection across the social
+    web that primarily uses direct indications of sentiment. The results from six diverse
+    social web data sets (MySpace, Twitter, YouTube, Digg, RunnersWorld, BBCForums) indicate
+    that SentiStrength 2 is successful in the sense of performing better than a baseline
+    approach for all data sets in both supervised and unsupervised cases. SentiStrength
+    is not always better than machine-learning approaches that exploit indirect indicators
+    of sentiment, however, and is particularly weaker for positive sentiment in news-related
+    discussions. Overall, the results suggest that, even unsupervised, SentiStrength is
+    robust enough to be applied to a wide variety of different social web contexts.},
+    journal = {J. Am. Soc. Inf. Sci. Technol.},
+    month = jan,
+    pages = {163–173},
+    numpages = {11}
+}
+
+
+
+

Dataset id: en_silicone_meld_s

+
    +
  • Domain: chats
    • +
  • Language: en
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@inproceedings{dataset_en_silicone,
+    title = "Hierarchical Pre-training for Sequence Labelling in Spoken Dialog",
+    author = "Chapuis, Emile  and
+        Colombo, Pierre  and
+        Manica, Matteo  and
+        Labeau, Matthieu  and
+        Clavel, Chlo{\'e}",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.findings-emnlp.239",
+    doi = "10.18653/v1/2020.findings-emnlp.239",
+    pages = "2636--2648",
+}
+
+
+
+

Dataset id: en_silicone_sem

+
    +
  • Domain: chats
    • +
  • Language: en
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@inproceedings{dataset_en_silicone,
+    title = "Hierarchical Pre-training for Sequence Labelling in Spoken Dialog",
+    author = "Chapuis, Emile  and
+        Colombo, Pierre  and
+        Manica, Matteo  and
+        Labeau, Matthieu  and
+        Clavel, Chlo{\'e}",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.findings-emnlp.239",
+    doi = "10.18653/v1/2020.findings-emnlp.239",
+    pages = "2636--2648",
+}
+
+
+
+

Dataset id: en_tweet_airlines

+
    +
  • Domain: social_media
    • +
  • Language: en
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@misc{dataset_en_tweet_airlines,
+    url={https://www.kaggle.com/crowdflower/twitter-airline-sentiment},
+    author={Crowdflower Inc.},
+    title={Twitter US Airline Sentiment},
+    year={2015}
+}
+
+
+
+

Dataset id: en_tweets_sanders

+
    +
  • Domain: social_media
    • +
  • Language: en
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@article{dataset_en_tweets_sanders,
+    title={{Sanders-Twitter Sentiment Corpus}},
+    author={Sanders, Niek J},
+    journal={Sanders Analytics LLC},
+    year={2011}
+}
+
+
+
+

Dataset id: en_twitter_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: en
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@article{dataset_twitter_sentiment,
+    doi = {10.1371/journal.pone.0155036},
+    author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},
+    journal = {PLOS ONE},
+    publisher = {Public Library of Science},
+    title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},
+    year = {2016},
+    month = {05},
+    volume = {11},
+    url = {https://doi.org/10.1371/journal.pone.0155036},
+    pages = {1-26},
+    number = {5},
+}
+
+
+
+

Dataset id: en_vader_amazon

+
    +
  • Domain: reviews
    • +
  • Language: en
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@inproceedings{dataset_en_vader,
+    title={{VADER}: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text},
+    author={Clayton J. Hutto and Eric Gilbert},
+    booktitle={Proceedings of the International AAAI Conference on Web and Social Media},
+    year={2014},
+    url={https://ojs.aaai.org/index.php/ICWSM/article/view/14550},
+    month={May}, 
+    pages={216-225},
+    volume=8,
+}
+
+
+
+

Dataset id: en_vader_movie_reviews

+
    +
  • Domain: reviews
    • +
  • Language: en
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@inproceedings{dataset_en_vader,
+    title={{VADER}: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text},
+    author={Clayton J. Hutto and Eric Gilbert},
+    booktitle={Proceedings of the International AAAI Conference on Web and Social Media},
+    year={2014},
+    url={https://ojs.aaai.org/index.php/ICWSM/article/view/14550},
+    month={May}, 
+    pages={216-225},
+    volume=8,
+}
+
+
+
+

Dataset id: en_vader_nyt

+
    +
  • Domain: news
    • +
  • Language: en
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@inproceedings{dataset_en_vader,
+    title={{VADER}: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text},
+    author={Clayton J. Hutto and Eric Gilbert},
+    booktitle={Proceedings of the International AAAI Conference on Web and Social Media},
+    year={2014},
+    url={https://ojs.aaai.org/index.php/ICWSM/article/view/14550},
+    month={May}, 
+    pages={216-225},
+    volume=8,
+}
+
+
+
+

Dataset id: en_vader_twitter

+
    +
  • Domain: social_media
    • +
  • Language: en
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@inproceedings{dataset_en_vader,
+    title={{VADER}: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text},
+    author={Clayton J. Hutto and Eric Gilbert},
+    booktitle={Proceedings of the International AAAI Conference on Web and Social Media},
+    year={2014},
+    url={https://ojs.aaai.org/index.php/ICWSM/article/view/14550},
+    month={May}, 
+    pages={216-225},
+    volume=8,
+}
+
+
+
+

Dataset id: es_muchocine

+
    +
  • Domain: reviews
    • +
  • Language: es
    • +
  • Language family: Indo-European
    • +
  • Genus: Romance
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@article{dataset_es_muchocine,
+    title={Experiments in sentiment classification of movie reviews in Spanish},
+    author={Cruz, Fermin L and Troyano, Jose A and Enriquez, Fernando and Ortega, Javier},
+    journal={Procesamiento del Lenguaje Natural},
+    volume={41},
+    pages={73--80},
+    year={2008},
+    publisher={SOC ESPANOLA PROCESAMIENTO LENGUAJE NATURAL-SEPLN DEPT LENGUAJES \& SISTEMAS~…}
+}
+
+
+
+

Dataset id: es_multilan_amazon

+
    +
  • Domain: reviews
    • +
  • Language: es
    • +
  • Language family: Indo-European
    • +
  • Genus: Romance
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@inproceedings{dataset_multilan_amazon,
+    title = "The Multilingual {A}mazon Reviews Corpus",
+    author = {Keung, Phillip  and
+        Lu, Yichao  and
+        Szarvas, Gy{\"o}rgy  and
+        Smith, Noah A.},
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.emnlp-main.369",
+    doi = "10.18653/v1/2020.emnlp-main.369",
+    pages = "4563--4568",
+}
+
+
+
+

Dataset id: es_paper_reviews

+
    +
  • Domain: reviews
    • +
  • Language: es
    • +
  • Language family: Indo-European
    • +
  • Genus: Romance
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@article{dataset_es_paper_reviews,
+    author = {Keith Norambuena, Brian and Lettura, Exequiel and Villegas, Claudio},
+    year = {2019},
+    month = {02},
+    pages = {191-214},
+    title = {Sentiment analysis and opinion mining applied to scientific paper reviews},
+    volume = {23},
+    journal = {Intelligent Data Analysis},
+    doi = {10.3233/IDA-173807}
+}
+
+
+
+

Dataset id: es_semeval2020

+
    +
  • Domain: social_media
    • +
  • Language: es
    • +
  • Language family: Indo-European
    • +
  • Genus: Romance
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@inproceedings{dataset_semeval_2020,
+    title = "{S}em{E}val-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets",
+    author = {Patwa, Parth  and
+        Aguilar, Gustavo  and
+        Kar, Sudipta  and
+        Pandey, Suraj  and
+        PYKL, Srinivas  and
+        Gamb{\"a}ck, Bj{\"o}rn  and
+        Chakraborty, Tanmoy  and
+        Solorio, Thamar  and
+        Das, Amitava},
+    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
+    month = dec,
+    year = "2020",
+    address = "Barcelona (online)",
+    publisher = "International Committee for Computational Linguistics",
+    url = "https://aclanthology.org/2020.semeval-1.100",
+    doi = "10.18653/v1/2020.semeval-1.100",
+    pages = "774--790",
+}
+
+
+
+

Dataset id: es_twitter_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: es
    • +
  • Language family: Indo-European
    • +
  • Genus: Romance
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@article{dataset_twitter_sentiment,
+    doi = {10.1371/journal.pone.0155036},
+    author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},
+    journal = {PLOS ONE},
+    publisher = {Public Library of Science},
+    title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},
+    year = {2016},
+    month = {05},
+    volume = {11},
+    url = {https://doi.org/10.1371/journal.pone.0155036},
+    pages = {1-26},
+    number = {5},
+}
+
+
+
+

Dataset id: fa_sentipers

+
    +
  • Domain: reviews
    • +
  • Language: fa
    • +
  • Language family: Indo-European
    • +
  • Genus: Iranian
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SOV
    • +
  • Negative morphemes: negative affix
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: MorphNeg
    • +
  • Prefixing vs suffixing: weakly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@article{dataset_fa_sentipers,
+    author    = {Pedram Hosseini and
+                Ali Ahmadian Ramaki and
+                Hassan Maleki and
+                Mansoureh Anvari and
+                Seyed Abolghasem Mirroshandel},
+    title     = {{SentiPers}: {A} Sentiment Analysis Corpus for Persian},
+    journal   = {Computing Research Repository},
+    volume    = {arXiv:1801.07737},
+    note = {Version 2},
+    year      = {2018},
+    url       = {http://arxiv.org/abs/1801.07737},
+    eprinttype = {arXiv},
+    eprint    = {1801.07737},
+    timestamp = {Mon, 13 Aug 2018 16:47:47 +0200},
+    biburl    = {https://dblp.org/rec/journals/corr/abs-1801-07737.bib},
+    bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+
+
+
+

Dataset id: fr_dai_labor

+
    +
  • Domain: social_media
    • +
  • Language: fr
    • +
  • Language family: Indo-European
    • +
  • Genus: Romance
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: OptDoubleNeg
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@inproceedings{dataset_dai_labor,
+    author = {Narr, Sascha  and Michael Hülfenhaus and  Albayrak, Sahin},
+    title = {Language-Independent Twitter Sentiment Analysis},
+    booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},
+    year = {2012},
+    location = {Dortmund, Germany},
+}
+
+
+
+

Dataset id: fr_ifeel

+
    +
  • Domain: social_media
    • +
  • Language: fr
    • +
  • Language family: Indo-European
    • +
  • Genus: Romance
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: OptDoubleNeg
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@inproceedings{dataset_dai_labor,
+    author = {Narr, Sascha  and Michael Hülfenhaus and  Albayrak, Sahin},
+    title = {Language-Independent Twitter Sentiment Analysis},
+    booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},
+    year = {2012},
+    location = {Dortmund, Germany},
+}
+
+
+
+

Dataset id: fr_multilan_amazon

+
    +
  • Domain: reviews
    • +
  • Language: fr
    • +
  • Language family: Indo-European
    • +
  • Genus: Romance
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: OptDoubleNeg
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@inproceedings{dataset_multilan_amazon,
+    title = "The Multilingual {A}mazon Reviews Corpus",
+    author = {Keung, Phillip  and
+        Lu, Yichao  and
+        Szarvas, Gy{\"o}rgy  and
+        Smith, Noah A.},
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.emnlp-main.369",
+    doi = "10.18653/v1/2020.emnlp-main.369",
+    pages = "4563--4568",
+}
+
+
+
+

Dataset id: he_hebrew_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: he
    • +
  • Language family: Afro-Asiatic
    • +
  • Genus: Semitic
    • +
  • Definite articles: definite affix
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: weakly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@inproceedings{dataset_he_hebrew_sentiment,
+    title = "Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from {M}odern {H}ebrew",
+    author = "Amram, Adam  and
+        Ben David, Anat  and
+        Tsarfaty, Reut",
+    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
+    month = aug,
+    year = "2018",
+    address = "Santa Fe, New Mexico, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/C18-1190",
+    pages = "2242--2252",
+    abstract = "This paper empirically studies the effects of representation choices on neural sentiment analysis for Modern Hebrew, a morphologically rich language (MRL) for which no sentiment analyzer currently exists. We study two dimensions of representational choices: (i) the granularity of the input signal (token-based vs. morpheme-based), and (ii) the level of encoding of vocabulary items (string-based vs. character-based). We hypothesise that for MRLs, languages where multiple meaning-bearing elements may be carried by a single space-delimited token, these choices will have measurable effects on task perfromance, and that these effects may vary for different architectural designs {---} fully-connected, convolutional or recurrent. Specifically, we hypothesize that morpheme-based representations will have advantages in terms of their generalization capacity and task accuracy, due to their better OOV coverage. To empirically study these effects, we develop a new sentiment analysis benchmark for Hebrew, based on 12K social media comments, and provide two instances of these data: in token-based and morpheme-based settings. Our experiments show that representation choices empirical effects vary with architecture type. While fully-connected and convolutional networks slightly prefer token-based settings, RNNs benefit from a morpheme-based representation, in accord with the hypothesis that explicit morphological information may help generalize. Our endeavour also delivers the first state-of-the-art broad-coverage sentiment analyzer for Hebrew, with over 89{\%} accuracy, alongside an established benchmark to further study the effects of linguistic representation choices on neural networks{'} task performance.",
+}
+
+
+
+

Dataset id: hi_semeval2020

+
    +
  • Domain: social_media
    • +
  • Language: hi
    • +
  • Language family: Indo-European
    • +
  • Genus: Indic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 3
    • +
  • Order of subject, object, verb: SOV
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SONegV
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@inproceedings{dataset_semeval_2020,
+    title = "{S}em{E}val-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets",
+    author = {Patwa, Parth  and
+        Aguilar, Gustavo  and
+        Kar, Sudipta  and
+        Pandey, Suraj  and
+        PYKL, Srinivas  and
+        Gamb{\"a}ck, Bj{\"o}rn  and
+        Chakraborty, Tanmoy  and
+        Solorio, Thamar  and
+        Das, Amitava},
+    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
+    month = dec,
+    year = "2020",
+    address = "Barcelona (online)",
+    publisher = "International Committee for Computational Linguistics",
+    url = "https://aclanthology.org/2020.semeval-1.100",
+    doi = "10.18653/v1/2020.semeval-1.100",
+    pages = "774--790",
+}
+
+
+
+

Dataset id: hr_sentiment_news_document

+
    +
  • Domain: news
    • +
  • Language: hr
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 5
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: other
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@Article{dataset_hr_sentiment_news_document,
+    AUTHOR = {Pelicon, Andraž and Pranjić, Marko and Miljković, Dragana and Škrlj, Blaž and Pollak, Senja},
+    TITLE = {Zero-Shot Learning for Cross-Lingual News Sentiment Classification},
+    JOURNAL = {Applied Sciences},
+    VOLUME = {10},
+    YEAR = {2020},
+    NUMBER = {17},
+    ARTICLE-NUMBER = {5993},
+    URL = {https://www.mdpi.com/2076-3417/10/17/5993},
+    ISSN = {2076-3417},
+    ABSTRACT = {In this paper, we address the task of zero-shot cross-lingual news sentiment classification. Given the annotated dataset of positive, neutral, and negative news in Slovene, the aim is to develop a news classification system that assigns the sentiment category not only to Slovene news, but to news in another language without any training data required. Our system is based on the multilingual BERTmodel, while we test different approaches for handling long documents and propose a novel technique for sentiment enrichment of the BERT model as an intermediate training step. With the proposed approach, we achieve state-of-the-art performance on the sentiment analysis task on Slovenian news. We evaluate the zero-shot cross-lingual capabilities of our system on a novel news sentiment test set in Croatian. The results show that the cross-lingual approach also largely outperforms the majority classifier, as well as all settings without sentiment enrichment in pre-training.},
+    DOI = {10.3390/app10175993}
+}
+
+
+
+

Dataset id: hr_twitter_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: hr
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 5
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: other
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@article{dataset_twitter_sentiment,
+    doi = {10.1371/journal.pone.0155036},
+    author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},
+    journal = {PLOS ONE},
+    publisher = {Public Library of Science},
+    title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},
+    year = {2016},
+    month = {05},
+    volume = {11},
+    url = {https://doi.org/10.1371/journal.pone.0155036},
+    pages = {1-26},
+    number = {5},
+}
+
+
+
+

Dataset id: hu_twitter_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: hu
    • +
  • Language family: Uralic
    • +
  • Genus: Ugric
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 10 or more
    • +
  • Order of subject, object, verb: no dominant order
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@article{dataset_twitter_sentiment,
+    doi = {10.1371/journal.pone.0155036},
+    author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},
+    journal = {PLOS ONE},
+    publisher = {Public Library of Science},
+    title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},
+    year = {2016},
+    month = {05},
+    volume = {11},
+    url = {https://doi.org/10.1371/journal.pone.0155036},
+    pages = {1-26},
+    number = {5},
+}
+
+
+
+

Dataset id: it_evalita2016

+
    +
  • Domain: social_media
    • +
  • Language: it
    • +
  • Language family: Indo-European
    • +
  • Genus: Romance
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative intonation only
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@inproceedings{dataset_it_evalita2016,
+    TITLE = {{Overview of the Evalita 2016 SENTIment POLarity Classification Task}},
+    AUTHOR = {Barbieri, Francesco and Basile, Valerio and Croce, Danilo and Nissim, Malvina and Novielli, Nicole and Patti, Viviana},
+    URL = {https://hal.inria.fr/hal-01414731},
+    BOOKTITLE = {{Proceedings of Third Italian Conference on Computational Linguistics (CLiC-it 2016) \& Fifth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2016)}},
+    ADDRESS = {Naples, Italy},
+    YEAR = {2016},
+    MONTH = Dec,
+    KEYWORDS = {Natural language processing and web ; Social media analysis ; Sentiment analysis},
+    PDF = {https://hal.inria.fr/hal-01414731/file/paper_026.pdf},
+    HAL_ID = {hal-01414731},
+    HAL_VERSION = {v1},
+}
+
+
+
+

Dataset id: it_multiemotions

+
    +
  • Domain: social_media
    • +
  • Language: it
    • +
  • Language family: Indo-European
    • +
  • Genus: Romance
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative intonation only
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@inproceedings{dataset_it_multiemotions,
+    author = {Sprugnoli, Rachele},
+    year = {2020},
+    month = {12},
+    pages = {},
+    title = {MultiEmotions-It: a New Dataset for Opinion Polarity and Emotion Analysis for Italian},
+    booktitle = {Proceedings of the Seventh Italian Conference on Computational Linguistics},
+}
+
+
+
+

Dataset id: ja_multilan_amazon

+
    +
  • Domain: reviews
    • +
  • Language: ja
    • +
  • Language family: Japanese
    • +
  • Genus: Japanese
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 8-9
    • +
  • Order of subject, object, verb: SOV
    • +
  • Negative morphemes: negative affix
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: MorphNeg
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: no grammatical gender
    • +
+
@inproceedings{dataset_multilan_amazon,
+    title = "The Multilingual {A}mazon Reviews Corpus",
+    author = {Keung, Phillip  and
+        Lu, Yichao  and
+        Szarvas, Gy{\"o}rgy  and
+        Smith, Noah A.},
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.emnlp-main.369",
+    doi = "10.18653/v1/2020.emnlp-main.369",
+    pages = "4563--4568",
+}
+
+
+
+

Dataset id: lv_ltec_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: lv
    • +
  • Language family: Indo-European
    • +
  • Genus: Baltic
    • +
  • Definite articles: demonstrative word used as definite article
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: 5
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative affix
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: MorphNeg
    • +
  • Prefixing vs suffixing: weakly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@article{dataset_lv_ltec_sentiment,
+    author    = {Uga Sprogis and
+                Matiss Rikters},
+    title     = {What Can We Learn From Almost a Decade of Food Tweets},
+    journal   = {Computing Research Repository},
+    volume    = {arXiv:2007.05194},
+    note = {Version 2},
+    year      = {2020},
+    url       = {https://arxiv.org/abs/2007.05194},
+    eprinttype = {arXiv},
+    eprint    = {2007.05194},
+    timestamp = {Mon, 20 Jul 2020 14:20:39 +0200},
+    biburl    = {https://dblp.org/rec/journals/corr/abs-2007-05194.bib},
+    bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+
+
+
+

Dataset id: pl_klej_allegro_reviews

+
    +
  • Domain: reviews
    • +
  • Language: pl
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 6-7
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@inproceedings{dataset_pl_klej_allegro_reviews,
+    title = "{KLEJ}: Comprehensive Benchmark for {P}olish Language Understanding",
+    author = "Rybak, Piotr  and
+        Mroczkowski, Robert  and
+        Tracz, Janusz  and
+        Gawlik, Ireneusz",
+    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
+    month = jul,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.acl-main.111",
+    doi = "10.18653/v1/2020.acl-main.111",
+    pages = "1191--1201",
+}
+
+
+
+

Dataset id: pl_opi_lil_2012

+
    +
  • Domain: social_media
    • +
  • Language: pl
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 6-7
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@article{dataset_pl_opi_lil_2012,
+    author = {Pawel Sobkowicz and Antoni Sobkowicz},
+    title ={Two-Year Study of Emotion and Communication Patterns in a Highly Polarized Political Discussion Forum},
+    journal = {Social Science Computer Review},
+    volume = {30},
+    number = {4},
+    pages = {448-469},
+    year = {2012},
+    doi = {10.1177/0894439312436512}
+}
+
+
+
+

Dataset id: pl_polemo

+
    +
  • Domain: reviews
    • +
  • Language: pl
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 6-7
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@inproceedings{dataset_pl_polemo,
+    title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews",
+    author = "Koco{\'n}, Jan  and
+        Mi{\l}kowski, Piotr  and
+        Za{\'s}ko-Zieli{\'n}ska, Monika",
+    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
+    month = nov,
+    year = "2019",
+    address = "Hong Kong, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/K19-1092",
+    doi = "10.18653/v1/K19-1092",
+    pages = "980--991"
+}
+
+
+
+

Dataset id: pl_twitter_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: pl
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 6-7
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@article{dataset_twitter_sentiment,
+    doi = {10.1371/journal.pone.0155036},
+    author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},
+    journal = {PLOS ONE},
+    publisher = {Public Library of Science},
+    title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},
+    year = {2016},
+    month = {05},
+    volume = {11},
+    url = {https://doi.org/10.1371/journal.pone.0155036},
+    pages = {1-26},
+    number = {5},
+}
+
+
+
+

Dataset id: pt_dai_labor

+
    +
  • Domain: social_media
    • +
  • Language: pt
    • +
  • Language family: Indo-European
    • +
  • Genus: Romance
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@inproceedings{dataset_dai_labor,
+    author = {Narr, Sascha  and Michael Hülfenhaus and  Albayrak, Sahin},
+    title = {Language-Independent Twitter Sentiment Analysis},
+    booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},
+    year = {2012},
+    location = {Dortmund, Germany},
+}
+
+
+
+

Dataset id: pt_ifeel

+
    +
  • Domain: social_media
    • +
  • Language: pt
    • +
  • Language family: Indo-European
    • +
  • Genus: Romance
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@inproceedings{dataset_dai_labor,
+    author = {Narr, Sascha  and Michael Hülfenhaus and  Albayrak, Sahin},
+    title = {Language-Independent Twitter Sentiment Analysis},
+    booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},
+    year = {2012},
+    location = {Dortmund, Germany},
+}
+
+
+
+

Dataset id: pt_tweet_sent_br

+
    +
  • Domain: social_media
    • +
  • Language: pt
    • +
  • Language family: Indo-European
    • +
  • Genus: Romance
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@inproceedings{dataset_pt_tweet_sent_br,
+    title = "Building a Sentiment Corpus of Tweets in {B}razilian {P}ortuguese",
+    author = "Brum, Henrico  and
+        Volpe Nunes, Maria das Gra{\c{c}}as",
+    booktitle = "Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)",
+    month = may,
+    year = "2018",
+    address = "Miyazaki, Japan",
+    publisher = "European Language Resources Association (ELRA)",
+    url = "https://aclanthology.org/L18-1658",
+}
+
+
+
+

Dataset id: pt_twitter_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: pt
    • +
  • Language family: Indo-European
    • +
  • Genus: Romance
    • +
  • Definite articles: definite word distinct from demonstrative
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@article{dataset_twitter_sentiment,
+    doi = {10.1371/journal.pone.0155036},
+    author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},
+    journal = {PLOS ONE},
+    publisher = {Public Library of Science},
+    title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},
+    year = {2016},
+    month = {05},
+    volume = {11},
+    url = {https://doi.org/10.1371/journal.pone.0155036},
+    pages = {1-26},
+    number = {5},
+}
+
+
+
+

Dataset id: ru_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: ru
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 6-7
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@inproceedings{dataset_ru_sentiment,
+    title = "{R}u{S}entiment: An Enriched Sentiment Analysis Dataset for Social Media in {R}ussian",
+    author = "Rogers, Anna  and
+        Romanov, Alexey  and
+        Rumshisky, Anna  and
+        Volkova, Svitlana  and
+        Gronas, Mikhail  and
+        Gribov, Alex",
+    booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
+    month = aug,
+    year = "2018",
+    address = "Santa Fe, New Mexico, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/C18-1064",
+    pages = "755--763",
+    abstract = "This paper presents RuSentiment, a new dataset for sentiment analysis of social media posts in Russian, and a new set of comprehensive annotation guidelines that are extensible to other languages. RuSentiment is currently the largest in its class for Russian, with 31,185 posts annotated with Fleiss{'} kappa of 0.58 (3 annotations per post). To diversify the dataset, 6,950 posts were pre-selected with an active learning-style strategy. We report baseline classification results, and we also release the best-performing embeddings trained on 3.2B tokens of Russian VKontakte posts.",
+}
+
+
+
+

Dataset id: ru_twitter_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: ru
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 6-7
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@article{dataset_twitter_sentiment,
+    doi = {10.1371/journal.pone.0155036},
+    author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},
+    journal = {PLOS ONE},
+    publisher = {Public Library of Science},
+    title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},
+    year = {2016},
+    month = {05},
+    volume = {11},
+    url = {https://doi.org/10.1371/journal.pone.0155036},
+    pages = {1-26},
+    number = {5},
+}
+
+
+
+

Dataset id: sk_twitter_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: sk
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 6-7
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative affix
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: MorphNeg
    • +
  • Prefixing vs suffixing: weakly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@article{dataset_twitter_sentiment,
+    doi = {10.1371/journal.pone.0155036},
+    author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},
+    journal = {PLOS ONE},
+    publisher = {Public Library of Science},
+    title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},
+    year = {2016},
+    month = {05},
+    volume = {11},
+    url = {https://doi.org/10.1371/journal.pone.0155036},
+    pages = {1-26},
+    number = {5},
+}
+
+
+
+

Dataset id: sl_sentinews

+
    +
  • Domain: news
    • +
  • Language: sl
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 6-7
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@Article{Bučar2018,
+    author={Bu{\v{c}}ar, Jo{\v{z}}e
+    and {\v{Z}}nidar{\v{s}}i{\v{c}}, Martin
+    and Povh, Janez},
+    title={Annotated news corpora and a lexicon for sentiment analysis in Slovene},
+    journal={Language Resources and Evaluation},
+    year={2018},
+    month={Sep},
+    day={01},
+    volume={52},
+    number={3},
+    pages={895-919},
+    abstract={In this study, we introduce Slovene web-crawled news corpora with sentiment annotation on three levels of granularity: sentence, paragraph and document levels. We describe the methodology and tools that were required for their construction. The corpora contain more than 250,000 documents with political, business, economic and financial content from five Slovene media resources on the web. More than 10,000 of them were manually annotated as negative, neutral or positive. All corpora are publicly available under a Creative Commons copyright license. We used the annotated documents to construct a Slovene sentiment lexicon, which is the first of its kind for Slovene, and to assess the sentiment classification approaches used. The constructed corpora were also utilised to monitor within-the-document sentiment dynamics, its changes over time and relations with news topics. We show that sentiment is, on average, more explicit at the beginning of documents, and it loses sharpness towards the end of documents.},
+    issn={1574-0218},
+    doi={10.1007/s10579-018-9413-3},
+    url={https://doi.org/10.1007/s10579-018-9413-3}
+}
+
+
+
+

Dataset id: sl_twitter_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: sl
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 6-7
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@article{dataset_twitter_sentiment,
+    doi = {10.1371/journal.pone.0155036},
+    author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},
+    journal = {PLOS ONE},
+    publisher = {Public Library of Science},
+    title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},
+    year = {2016},
+    month = {05},
+    volume = {11},
+    url = {https://doi.org/10.1371/journal.pone.0155036},
+    pages = {1-26},
+    number = {5},
+}
+
+
+
+

Dataset id: sq_twitter_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: sq
    • +
  • Language family: Indo-European
    • +
  • Genus: Albanian
    • +
  • Definite articles: definite affix
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: 4
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@article{dataset_twitter_sentiment,
+    doi = {10.1371/journal.pone.0155036},
+    author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},
+    journal = {PLOS ONE},
+    publisher = {Public Library of Science},
+    title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},
+    year = {2016},
+    month = {05},
+    volume = {11},
+    url = {https://doi.org/10.1371/journal.pone.0155036},
+    pages = {1-26},
+    number = {5},
+}
+
+
+
+

Dataset id: sr_movie_reviews

+
    +
  • Domain: reviews
    • +
  • Language: sr
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 5
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: other
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@inproceedings{dataset_sr_serb_movie_reviews,
+    title = "Reliable Baselines for Sentiment Analysis in Resource-Limited Languages: The {S}erbian Movie Review Dataset",
+    author = "Batanovi{\'c}, Vuk  and
+        Nikoli{\'c}, Bo{\v{s}}ko  and
+        Milosavljevi{\'c}, Milan",
+    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
+    month = may,
+    year = "2016",
+    address = "Portoro{\v{z}}, Slovenia",
+    publisher = "European Language Resources Association (ELRA)",
+    url = "https://aclanthology.org/L16-1427",
+    pages = "2688--2696",
+    abstract = "Collecting data for sentiment analysis in resource-limited languages carries a significant risk of sample selection bias, since the small quantities of available data are most likely not representative of the whole population. Ignoring this bias leads to less robust machine learning classifiers and less reliable evaluation results. In this paper we present a dataset balancing algorithm that minimizes the sample selection bias by eliminating irrelevant systematic differences between the sentiment classes. We prove its superiority over the random sampling method and we use it to create the Serbian movie review dataset ― SerbMR ― the first balanced and topically uniform sentiment analysis dataset in Serbian. In addition, we propose an incremental way of finding the optimal combination of simple text processing options and machine learning features for sentiment classification. Several popular classifiers are used in conjunction with this evaluation approach in order to establish strong but reliable baselines for sentiment analysis in Serbian.",
+}
+
+
+
+

Dataset id: sr_senticomments

+
    +
  • Domain: reviews
    • +
  • Language: sr
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 5
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: other
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@article{dataset_sr_senticomments,
+    doi = {10.1371/journal.pone.0242050},
+    author = {Batanović, Vuk AND Cvetanović, Miloš AND Nikolić, Boško},
+    journal = {PLOS ONE},
+    publisher = {Public Library of Science},
+    title = {A versatile framework for resource-limited sentiment articulation, annotation, and analysis of short texts},
+    year = {2020},
+    month = {11},
+    volume = {15},
+    url = {https://doi.org/10.1371/journal.pone.0242050},
+    pages = {1-30},
+    abstract = {Choosing a comprehensive and cost-effective way of articulating and annotating the sentiment of a text is not a trivial task, particularly when dealing with short texts, in which sentiment can be expressed through a wide variety of linguistic and rhetorical phenomena. This problem is especially conspicuous in resource-limited settings and languages, where design options are restricted either in terms of manpower and financial means required to produce appropriate sentiment analysis resources, or in terms of available language tools, or both. In this paper, we present a versatile approach to addressing this issue, based on multiple interpretations of sentiment labels that encode information regarding the polarity, subjectivity, and ambiguity of a text, as well as the presence of sarcasm or a mixture of sentiments. We demonstrate its use on Serbian, a resource-limited language, via the creation of a main sentiment analysis dataset focused on movie comments, and two smaller datasets belonging to the movie and book domains. In addition to measuring the quality of the annotation process, we propose a novel metric to validate its cost-effectiveness. Finally, the practicality of our approach is further validated by training, evaluating, and determining the optimal configurations of several different kinds of machine-learning models on a range of sentiment classification tasks using the produced dataset.},
+    number = {11},
+}
+
+
+
+

Dataset id: sr_twitter_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: sr
    • +
  • Language family: Indo-European
    • +
  • Genus: Slavic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 5
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: other
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine, neuter
    • +
+
@article{dataset_twitter_sentiment,
+    doi = {10.1371/journal.pone.0155036},
+    author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},
+    journal = {PLOS ONE},
+    publisher = {Public Library of Science},
+    title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},
+    year = {2016},
+    month = {05},
+    volume = {11},
+    url = {https://doi.org/10.1371/journal.pone.0155036},
+    pages = {1-26},
+    number = {5},
+}
+
+
+
+

Dataset id: sv_twitter_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: sv
    • +
  • Language family: Indo-European
    • +
  • Genus: Germanic
    • +
  • Definite articles: definite affix
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: interrogative word order
    • +
  • Position of negative word wrt SOV: more than one position
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: common, neuter
    • +
+
@article{dataset_twitter_sentiment,
+    doi = {10.1371/journal.pone.0155036},
+    author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},
+    journal = {PLOS ONE},
+    publisher = {Public Library of Science},
+    title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},
+    year = {2016},
+    month = {05},
+    volume = {11},
+    url = {https://doi.org/10.1371/journal.pone.0155036},
+    pages = {1-26},
+    number = {5},
+}
+
+
+
+

Dataset id: th_wisesight_sentiment

+
    +
  • Domain: social_media
    • +
  • Language: th
    • +
  • Language family: Tai-Kadai
    • +
  • Genus: Kam-Tai
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative auxiliary verb
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: little affixation
    • +
  • Coding of nominal plurality: mixed morphological plural
    • +
  • Grammatical genders: noun classifiers
    • +
+
@misc{dataset_th_wisesight_sentiment,
+    author       = {Suriyawongkul, Arthit and
+                    Chuangsuwanich, Ekapol and
+                    Chormai, Pattarawat and
+                    Polpanumas, Charin},
+    title        = {PyThaiNLP/wisesight-sentiment: First release (v1.0)},
+    month        = sep,
+    year         = 2019,
+    publisher    = {Zenodo},
+    version      = {v1.0},
+    doi          = {10.5281/zenodo.3457447},
+    url          = {https://doi.org/10.5281/zenodo.3457447},
+    note = {Zenodo}
+}
+
+
+
+

Dataset id: th_wongnai_reviews

+
    +
  • Domain: reviews
    • +
  • Language: th
    • +
  • Language family: Tai-Kadai
    • +
  • Genus: Kam-Tai
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: indefinite word distinct from one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative auxiliary verb
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: little affixation
    • +
  • Coding of nominal plurality: mixed morphological plural
    • +
  • Grammatical genders: noun classifiers
    • +
+
@misc{dataset_th_wongnai_reviews,
+    author = {Ekkalak Thongthanomkul and Tanapol Nearunchorn and Yuwat Chuesathuchon},
+    title = {wongnai-corpus},
+    year = {2019},
+    publisher = {GitHub},
+    journal = {GitHub repository},
+    howpublished = {\url{https://github.com/wongnai/wongnai-corpus}}
+}
+
+
+
+

Dataset id: ur_roman_urdu

+
    +
  • Domain: mixed
    • +
  • Language: ur
    • +
  • Language family: Indo-European
    • +
  • Genus: Indic
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: no article
    • +
  • Number of cases: 2
    • +
  • Order of subject, object, verb: SOV
    • +
  • Negative morphemes: negative affix
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SONegV
    • +
  • Prefixing vs suffixing: strongly suffixing
    • +
  • Coding of nominal plurality: plural suffix
    • +
  • Grammatical genders: masculine, feminine
    • +
+
@InProceedings{dataset_ur_roman_urdu,
+    title     = "Performing Natural Language Processing on Roman Urdu Datasets",
+    author   = "Zareen Sharf and Saif Ur Rahman",
+    booktitle = "International Journal of Computer Science and Network Security",
+    volume    = "18",
+    pages     = "141-148",
+    year      = "2018",
+    url = {http://paper.ijcsns.org/07_book/201801/20180117.pdf}
+}
+
+
+
+

Dataset id: zh_hotel_reviews

+
    +
  • Domain: reviews
    • +
  • Language: zh
    • +
  • Language family: Sino-Tibetan
    • +
  • Genus: Chinese
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: little affixation
    • +
  • Coding of nominal plurality: no plural
    • +
  • Grammatical genders: noun classifiers
    • +
+
@inproceedings{dataset_zh_hotel_reviews,
+    title = "An Empirical Study on Sentiment Classification of {C}hinese Review using Word Embedding",
+    author = "Lin, Yiou  and
+        Lei, Hang  and
+        Wu, Jia  and
+        Li, Xiaoyu",
+    booktitle = "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
+    month = oct,
+    year = "2015",
+    address = "Shanghai, China",
+    url = "https://aclanthology.org/Y15-2030",
+    pages = "258--266",
+}
+
+
+

Dataset id: zh_multilan_amazon

+
    +
  • Domain: reviews
    • +
  • Language: zh
    • +
  • Language family: Sino-Tibetan
    • +
  • Genus: Chinese
    • +
  • Definite articles: no article
    • +
  • Indefinite articles: indefinite word same as one
    • +
  • Number of cases: no morphological case-making
    • +
  • Order of subject, object, verb: SVO
    • +
  • Negative morphemes: negative particle
    • +
  • Polar questions: question particle
    • +
  • Position of negative word wrt SOV: SNegVO
    • +
  • Prefixing vs suffixing: little affixation
    • +
  • Coding of nominal plurality: no plural
    • +
  • Grammatical genders: noun classifiers
    • +
+
@inproceedings{dataset_multilan_amazon,
+    title = "The Multilingual {A}mazon Reviews Corpus",
+    author = {Keung, Phillip  and
+        Lu, Yichao  and
+        Szarvas, Gy{\"o}rgy  and
+        Smith, Noah A.},
+    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    month = nov,
+    year = "2020",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2020.emnlp-main.369",
+    doi = "10.18653/v1/2020.emnlp-main.369",
+    pages = "4563--4568",
+}
+
+
+
+
+ + +
+ +
+ +
+ + + + \ No newline at end of file diff --git a/dataset_card.html b/dataset_card.html new file mode 100644 index 0000000..147db44 --- /dev/null +++ b/dataset_card.html @@ -0,0 +1,3153 @@ + + + + + + + + + + +mms_benchmark - MMS Dataset Card + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+
+
+

MMS Dataset Card

+
+
+ Dataset Card for https://huggingface.co/datasets/Brand24/mms +
+
+
+
+ + +
+ + + + +
+ + +
+ + +
+ + + +
+ + + + + +
+

Easiness of using

+

One of the key ideas behind creating our library of datasets was to prioritize ease of use for researchers. Recognizing the importance of accessibility and convenience, we chose the HuggingFace platform as the storage and distribution platform for the datasets. HuggingFace provides a user-friendly interface and a wide range of tools and resources, making it easy for researchers to access and utilize the datasets.

+

To further enhance usability, we took the initiative to gather all the necessary citations for the datasets included in our library. By unifying the citations, we aimed to simplify and expedite the process of generating citations for researchers who utilize our datasets. This step reduces the time and effort required for researchers to acknowledge the datasets’ sources properly.

+

However, it is essential to note that while we have taken steps to streamline the citation process, researchers should still independently verify the licenses of the datasets, especially if they intend to use them for purposes beyond strict academic research. Ensuring compliance with licensing requirements is crucial to maintaining ethical and legal data use standards.

+

Overall, our overarching goal in creating this unified corpus of datasets is accelerating academic sentiment analysis research. By providing a comprehensive collection of high-quality datasets and facilitating their accessibility, we aim to support researchers in exploring and advancing sentiment analysis techniques and methodologies.

+
+

Data ready to slice and dice and train a model

+

Our dataset is designed to be versatile and allows researchers to slice and dice the data for training and modeling according to their specific needs. Drawing from the field of linguistic typology, which examines the characteristics of languages, we have incorporated various linguistic features into our dataset selection process. These features include the text itself, sentiment labels, the original dataset source, domain, language, language family, genus, the presence or absence of definite and indefinite articles, the number of cases, word order, negative morphemes, polar questions, the position of negative morphemes, prefixing vs. suffixing, coding of nominal plurals, and grammatical genders. Researchers can easily access datasets that match their desired linguistic typology criteria by offering these features as filtering options in our library.

+

For instance, researchers can download datasets specific to Slavic languages with interrogative word order for polar questions or datasets from the Afro-Asiatic language family without morphological case-making. This flexibility empowers researchers to tailor their analyses and models to their linguistic interests and research questions.

+
+
import datasets
+
+mms_dataset = datasets.load_dataset("Brand24/mms")
+mms_dataset_df = mms_dataset["train"].to_pandas()
+
+

All features in dataset

+
+
mms_dataset_df.sample(5)
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
_idtextlabeloriginal_datasetdomainlanguageFamilyGenusDefinite articlesIndefinite articlesNumber of casesOrder of subject, object, verbNegative morphemesPolar questionsPosition of negative word wrt SOVPrefixing vs suffixingCoding of nominal pluralityGrammatical genderscleanlab_self_confidence
11170231117023hlucnost mi prijde uplne v pohode, pere dobre,...2cs_mall_product_reviewsreviewscsIndo-EuropeanSlavicno articleno article6-7SVOnegative affixinterrogative word orderMorphNegweakly suffixingplural suffixmasculine, feminine, neuter0.679376
824580824580“فندق جميل ولكن الخدمة جدا سيئه”. . الخدمة غير...0ar_hardreviewsarAfro-AsiaticSemiticdefinite affixno article3SVOnegative particleinterrogative intonation onlySNegVOweakly suffixingmixed morphological pluralmasculine, feminine0.725264
60145936014593刚开始不习惯…之后还挺好用的…很轻便 很细…调节长度也很方便2zh_multilan_amazonreviewszhSino-TibetanChineseno articleindefinite word same as oneno morphological case-makingSVOnegative particlequestion particleSNegVOlittle affixationno pluralnoun classifiers0.907645
53138725313872Чемпионы. И этим все сказано.2ru_sentimentsocial_mediaruIndo-EuropeanSlavicno articleno article6-7SVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine, neuter0.109386
42906324290632“@UnCharroDice: Y no ha de sobrar, quien con c...1es_twitter_sentimentsocial_mediaesIndo-EuropeanRomancedefinite word distinct from demonstrativeindefinite word same as oneno morphological case-makingSVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixmasculine, feminine0.164549
+ +
+
+
+
+
+

Linguistic Typology

+

The field of language typology focuses on studying the similarities and differences among languages. These differences can be categorized into phonological (sounds), syntactic (structures), lexical (vocabulary), and theoretical aspects. Linguistic typology analyzes the current state of languages, contrasting with genealogical linguistics, which examines historical relationships between languages.

+

Genealogical linguistics studies language families and genera. A language family consists of languages that share a common ancestral language, while genera are branches within a language family. The Indo-European family, for example, includes genera such as Slavic, Romance, Germanic, and Indic. Over 7000 languages are categorized into approximately 150 language families, with Indo-European, Sino-Tibetan, Turkic, Afro-Asiatic, Nilo-Saharan, Niger-Congo, and Eskimo-Aleut being some of the largest families.

+

Within linguistic typology, languages are described using various linguistic features. Our work focuses on sentiment classification and selects ten relevant features:

+
    +
  • text: The feature text represents the actual text of the sentiment dataset. It is of type string and contains the text samples or sentences for sentiment analysis.
    • +
  • label: The feature label corresponds to the sentiment labels of the text samples. It is of type ClassLabel and has three possible values: negative, neutral, and positive. These labels indicate the sentiment or emotional polarity associated with the text.
    • +
  • original_dataset: The feature original_dataset refers to the name or identifier of the original dataset from which the text samples were extracted. It is of type string and provides information about the source dataset.
    • +
  • domain: The feature domain represents the domain or topic of the sentiment dataset. It is of type string and provides context regarding the subject matter of the text samples.
    • +
  • language: The feature language indicates the language of the text samples in the sentiment dataset. It is of type string and specifies the language in which the text is written.
    • +
  • Family: The feature Family represents the language family to which a specific language belongs. It is of type string and provides information about the broader categorization of languages into language families.
    • +
  • Genus: The feature Genus corresponds to the genus or branch within a language family. It is of type string and indicates the specific subgrouping of languages within a language family.
    • +
  • Definite article: Half of the languages do not use the definite article, which signals uniqueness or definiteness of a concept.
    • +
  • Indefinite article: Half of the languages do not use the indefinite article, with some languages using a separate article or the numeral “one.”
    • +
  • Number of cases: Languages vary greatly in the number of morphological cases used.
    • +
  • Order of subject, verb, and object: Different languages have different word orderings, with variations like SOV, SVO, VSO, VOS, OVS, and OSV.
    • +
  • Negative morphemes: Negative morphemes indicate clausal negation in declarative sentences.
    • +
  • Polar questions: Questions with yes/no answers, which can be formed using question particles, interrogative morphology, or intonation.
    • +
  • Position of the negative morpheme: The position of the negative morpheme can vary in relation to subjects and objects.
    • +
  • Prefixing vs. suffixing: Languages differ in their use of prefixes and suffixes in inflectional morphology.
    • +
  • Coding of nominal plurals: Plurals can be expressed through morphological changes or the use of plurality indicator morphemes.
    • +
  • Grammatical genders: Languages vary in the number of grammatical genders used, or may not use the concept at all.
    • +
+

These language features are available as filtering options in our library. Users can download specific facets of the collection, such as datasets in Slavic languages with interrogative word order for polar questions or datasets from the Afro-Asiatic language family without morphological case-making.

+
+
+
+

Datasheets for Datasets

+

The datasheets provide detailed information about the datasets, including data collection methods, annotation guidelines, and potential biases. They also specify the intended uses and potential limitations of the datasets.

+

The initial pool of sentiment datasets was gathered through an extensive search using sources such as Google Scholar, GitHub repositories, and the HuggingFace datasets library. This search yielded a total of 345 datasets.

+

To ensure the quality of the datasets, a set of quality assurance criteria was applied to manually filter the initial pool of datasets. The following criteria were used:

+
    +
  1. Strong Annotations: Datasets containing weak annotations, such as labels based on emoji occurrence or automatically generated through classification by machine learning models, were rejected. This decision was made to minimize the presence of noise in the datasets, ensuring higher quality annotations.
    2. +
  2. Well-Defined Annotation Protocol: Datasets without sufficient information about the annotation protocol, including whether the annotation was done manually or automatically and the number of annotators involved, were rejected. This step aimed to avoid merging datasets with contradicting annotation instructions, ensuring consistency across the selected datasets.
    3. +
  3. Numerical Ratings: Datasets with numerical ratings were accepted. Specifically, Likert-type 5-point scales were mapped into three class sentiment labels. Ratings 1 and 2 were mapped to “negative,” rating 3 was mapped to “neutral,” and ratings 4 and 5 were mapped to “positive.” This mapping allowed for consistent sentiment labeling across the datasets.
    4. +
  4. Three Classes Only: Datasets annotated with binary sentiment labels were rejected. The decision to focus on datasets with three sentiment classes (negative, neutral, and positive) was made based on the unsatisfactory performance of binary sentiment labeling in three-class settings.
    5. +
  5. Monolingual Datasets: In cases where a dataset contained samples in multiple languages, it was divided into independent datasets for each constituent language. This approach ensured that the corpus includes separate datasets for different languages, allowing for targeted analysis and evaluation.
    6. +
+

By applying these quality assurance criteria, we were able to filter the initial pool of sentiment datasets and select a final set of 79 datasets that met the specified standards for inclusion in the multilingual corpus.

+
+
f"We cover {mms_dataset_df.original_dataset.nunique()} datasets in {mms_dataset_df.language.nunique()} languages."
+
+
'We cover 79 datasets in 27 languages.'
+
+
+
+
f"The classes that we cover: {mms_dataset_df.label_name.unique()}"
+
+
"The classes that we cover: ['positive' 'neutral' 'negative']"
+
+
+
+
+

Limitations

+

Despite the fact that our collection is the largest public collection of multilingual sentiment datasets, it still covers only 27 languages. The collection of datasets is highly biased towards the Indo-European family of languages, English in particular. We attribute this bias to the general culture of scientific publishing and its enforcement of English as the primary carrier of scientific discovery. Our work’s main potential negative social impact is that the models developed and trained using the provided datasets may still exhibit better performance for the major languages. This could further perpetuate the existing language disparities and inequality in sentiment analysis capabilities across different languages. Addressing this limitation and working towards more equitable representation and performance across languages is crucial to avoid reinforcing language biases and the potential marginalization of underrepresented languages. The ethical implications of such disparities should be thoroughly discussed and considered.

+
+
+

+
Data Quality
+
+
+

An important limitation of our dataset collection is a significant variance in sample quality across all datasets and all languages. Above figure presents the distribution of self-confidence label-quality score for each data point computed by the cleanlab (Northcutt, Jiang, and Chuang 2021). The distribution of quality is skewed in favor of popular languages, with low-resource languages suffering from data quality issues. A related limitation is caused by an unequal distribution of data modalities across languages. For instance, our benchmark clearly shows that all models universally underperform when tested on Portuguese datasets. This is the direct result of the fact that data points for Portuguese almost exclusively represent the domain of social media. As a consequence, some combinations of filtering facets in our dataset collection produce very little data (i.e., asking for social media data in the Germanic genus of Indo-European languages will produce a significantly larger dataset than asking for news data representing Afro-Asiatic languages).

+

Finally, we acknowledge the lack of internal coherence of annotation protocols between datasets and languages. We have enforced strict quality criteria and rejected all datasets published without the annotation protocol, but we were unable, for obvious reasons, to unify annotation guidelines. The annotation of sentiment expressions and the assignment of sentiment labels are heavily subjective and, at the same time, influenced by cultural and linguistic features. Unfortunately, it is possible that semantically similar utterances will be assigned conflicting labels if they come from different datasets or modalities.

+
+

Filter examples by annotation qualitym

+

We know how imporant data quality is for the model training processes. Hence, we added cleanlab scores to each of 6M+ examples in all datasets. Now, it is enalbe to filter examples based on how good quality of data do you need for traning.

+

We can sort examples by top data quality. Cleanlab’s self confidence is a function to compute label-quality scores for classification datasets, where lower scores indicate labels less likely to be correct. Hence, for the best quality we want to have the highest scores.

+
+
clean_labels_data = mms_dataset_df.sort_values(by="cleanlab_self_confidence", ascending=False).head(10_000)
+
+
+
clean_labels_data.head()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
_idtextlabeloriginal_datasetdomainlanguageFamilyGenusDefinite articlesIndefinite articlesNumber of casesOrder of subject, object, verbNegative morphemesPolar questionsPosition of negative word wrt SOVPrefixing vs suffixingCoding of nominal pluralityGrammatical genderscleanlab_self_confidencelabel_name
30753023075302Great addition to any fan's yard! Show your te...2en_amazonreviewsenIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender0.999981positive
629922629922مخيب للأمل. . ىحَ0ar_hardreviewsarAfro-AsiaticSemiticdefinite affixno article3SVOnegative particleinterrogative intonation onlySNegVOweakly suffixingmixed morphological pluralmasculine, feminine0.999964negative
28582372858237This is a great flag to display your love of A...2en_amazonreviewsenIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender0.999950positive
31100313110031One of the best knives I now proudly own! Am a...2en_amazonreviewsenIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender0.999950positive
20529712052971Amen! My Savior Loves! Wonderful testimony!2en_amazonreviewsenIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender0.999948positive
+ +
+
+
+
+
+
+

Datasets

+

We added all necessary citations to the HuggingFace datasets card. You can find them inside citation key. We added a helper fuinctions to parse them.

+

We can load citations as strings - easy adding to bibtex.

+
+
from mms_benchmark.citations import get_citations
+
+
+
print(get_citations(mms_dataset["train"], citation_as_dict=False)["pl_polemo"])
+
+
@inproceedings{dataset_pl_polemo,
+    title = "Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews",
+    author = "Koco{\'n}, Jan  and
+        Mi{\l}kowski, Piotr  and
+        Za{\'s}ko-Zieli{\'n}ska, Monika",
+    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
+    month = nov,
+    year = "2019",
+    address = "Hong Kong, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/K19-1092",
+    doi = "10.18653/v1/K19-1092",
+    pages = "980--991"
+}
+% ------------------------------------------------------------------------------------------
+
+
+
+

Or as dictionary for working with them.

+
+
citations = get_citations(mms_dataset["train"], citation_as_dict=True)
+
+
+
citations["pl_polemo"]
+
+
{'pages': '980--991',
+ 'doi': '10.18653/v1/K19-1092',
+ 'url': 'https://aclanthology.org/K19-1092',
+ 'publisher': 'Association for Computational Linguistics',
+ 'address': 'Hong Kong, China',
+ 'year': '2019',
+ 'month': 'November',
+ 'booktitle': 'Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)',
+ 'author': "Koco{\\'n}, Jan  and\nMi{\\l}kowski, Piotr  and\nZa{\\'s}ko-Zieli{\\'n}ska, Monika",
+ 'title': 'Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews',
+ 'ENTRYTYPE': 'inproceedings',
+ 'ID': 'dataset_pl_polemo'}
+
+
+
+

Show all datasets with citations in a table

+
+
mms_dataset_df["citation"] = mms_dataset_df["original_dataset"].apply(lambda x: f'[@{citations[x]["ID"]}]')
+
+
+
mms_dataset_df[DATASET_COLS].drop_duplicates().sort_values("language").reset_index(drop=True)
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
languageoriginal_datasetdomainFamilyGenusDefinite articlesIndefinite articlesNumber of casesOrder of subject, object, verbNegative morphemesPolar questionsPosition of negative word wrt SOVPrefixing vs suffixingCoding of nominal pluralityGrammatical genderscitation
0arar_arsentdlsocial_mediaAfro-AsiaticSemiticdefinite affixno article3SVOnegative particleinterrogative intonation onlySNegVOweakly suffixingmixed morphological pluralmasculine, feminine[@dataset_ar_arsentdl]
1arar_semeval_2017mixedAfro-AsiaticSemiticdefinite affixno article3SVOnegative particleinterrogative intonation onlySNegVOweakly suffixingmixed morphological pluralmasculine, feminine[@dataset_semeval_2017]
2arar_oclarreviewsAfro-AsiaticSemiticdefinite affixno article3SVOnegative particleinterrogative intonation onlySNegVOweakly suffixingmixed morphological pluralmasculine, feminine[@dataset_ar_oclar]
3arar_labrreviewsAfro-AsiaticSemiticdefinite affixno article3SVOnegative particleinterrogative intonation onlySNegVOweakly suffixingmixed morphological pluralmasculine, feminine[@dataset_ar_labr]
4arar_syria_corpussocial_mediaAfro-AsiaticSemiticdefinite affixno article3SVOnegative particleinterrogative intonation onlySNegVOweakly suffixingmixed morphological pluralmasculine, feminine[@dataset_ar_bbn]
5arar_bradreviewsAfro-AsiaticSemiticdefinite affixno article3SVOnegative particleinterrogative intonation onlySNegVOweakly suffixingmixed morphological pluralmasculine, feminine[@dataset_ar_brad]
6arar_bbnsocial_mediaAfro-AsiaticSemiticdefinite affixno article3SVOnegative particleinterrogative intonation onlySNegVOweakly suffixingmixed morphological pluralmasculine, feminine[@dataset_ar_bbn]
7arar_astdsocial_mediaAfro-AsiaticSemiticdefinite affixno article3SVOnegative particleinterrogative intonation onlySNegVOweakly suffixingmixed morphological pluralmasculine, feminine[@dataset_ar_astd]
8arar_hardreviewsAfro-AsiaticSemiticdefinite affixno article3SVOnegative particleinterrogative intonation onlySNegVOweakly suffixingmixed morphological pluralmasculine, feminine[@dataset_ar_hard]
9bgbg_twitter_sentimentsocial_mediaIndo-EuropeanSlavicdefinite word distinct from demonstrativeno articleno morphological case-makingSVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_twitter_sentiment]
10bsbs_twitter_sentimentsocial_mediaIndo-EuropeanSlavicno articleno article5SVOnegative particlequestion particleotherstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_twitter_sentiment]
11cscs_facebooksocial_mediaIndo-EuropeanSlavicno articleno article6-7SVOnegative affixinterrogative word orderMorphNegweakly suffixingplural suffixmasculine, feminine, neuter[@dataset_cs_social_media]
12cscs_mall_product_reviewsreviewsIndo-EuropeanSlavicno articleno article6-7SVOnegative affixinterrogative word orderMorphNegweakly suffixingplural suffixmasculine, feminine, neuter[@dataset_cs_social_media]
13cscs_movie_reviewsreviewsIndo-EuropeanSlavicno articleno article6-7SVOnegative affixinterrogative word orderMorphNegweakly suffixingplural suffixmasculine, feminine, neuter[@dataset_cs_social_media]
14cscs_news_stancesocial_mediaIndo-EuropeanSlavicno articleno article6-7SVOnegative affixinterrogative word orderMorphNegweakly suffixingplural suffixmasculine, feminine, neuter[@dataset_cs_social_media]
15dede_twitter_sentimentsocial_mediaIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word same as one4no dominant ordernegative particleinterrogative word ordermore than one positionstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_twitter_sentiment]
16dede_ompsocial_mediaIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word same as one4no dominant ordernegative particleinterrogative word ordermore than one positionstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_de_omp]
17dede_sb10ksocial_mediaIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word same as one4no dominant ordernegative particleinterrogative word ordermore than one positionstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_de_sb10k]
18dede_ifeelsocial_mediaIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word same as one4no dominant ordernegative particleinterrogative word ordermore than one positionstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_dai_labor]
19dede_dai_laborsocial_mediaIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word same as one4no dominant ordernegative particleinterrogative word ordermore than one positionstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_dai_labor]
20dede_multilan_amazonreviewsIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word same as one4no dominant ordernegative particleinterrogative word ordermore than one positionstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_multilan_amazon]
21enen_vader_twittersocial_mediaIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender[@dataset_en_vader]
22enen_vader_nytnewsIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender[@dataset_en_vader]
23enen_vader_movie_reviewsreviewsIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender[@dataset_en_vader]
24enen_vader_amazonreviewsIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender[@dataset_en_vader]
25enen_twitter_sentimentsocial_mediaIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender[@dataset_twitter_sentiment]
26enen_tweets_sanderssocial_mediaIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender[@dataset_en_tweets_sanders]
27enen_tweet_airlinessocial_mediaIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender[@dataset_en_tweet_airlines]
28enen_silicone_semchatsIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender[@dataset_en_silicone]
29enen_sentistrengthsocial_mediaIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender[@dataset_en_sentistrength]
30enen_semeval_2017mixedIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender[@dataset_semeval_2017]
31enen_poem_sentimentpoemsIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender[@dataset_en_poem_sentiment]
32enen_per_sentnewsIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender[@dataset_en_per_sent]
33enen_multilan_amazonreviewsIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender[@dataset_multilan_amazon]
34enen_financial_phrasebank_sentences_75agreenewsIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender[@dataset_en_financial_phrasebank_sentences_75agree]
35enen_dai_laborsocial_mediaIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender[@dataset_dai_labor]
36enen_amazonreviewsIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender[@dataset_en_amazon]
37enen_silicone_meld_schatsIndo-EuropeanGermanicdefinite word distinct from demonstrativeindefinite word distinct from one2SVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixno grammatical gender[@dataset_en_silicone]
38eses_twitter_sentimentsocial_mediaIndo-EuropeanRomancedefinite word distinct from demonstrativeindefinite word same as oneno morphological case-makingSVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixmasculine, feminine[@dataset_twitter_sentiment]
39eses_semeval2020social_mediaIndo-EuropeanRomancedefinite word distinct from demonstrativeindefinite word same as oneno morphological case-makingSVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixmasculine, feminine[@dataset_semeval_2020]
40eses_multilan_amazonreviewsIndo-EuropeanRomancedefinite word distinct from demonstrativeindefinite word same as oneno morphological case-makingSVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixmasculine, feminine[@dataset_multilan_amazon]
41eses_muchocinereviewsIndo-EuropeanRomancedefinite word distinct from demonstrativeindefinite word same as oneno morphological case-makingSVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixmasculine, feminine[@dataset_es_muchocine]
42eses_paper_reviewsreviewsIndo-EuropeanRomancedefinite word distinct from demonstrativeindefinite word same as oneno morphological case-makingSVOnegative particleinterrogative word orderSNegVOstrongly suffixingplural suffixmasculine, feminine[@dataset_es_paper_reviews]
43fafa_sentipersreviewsIndo-EuropeanIranianno articleindefinite word same as one2SOVnegative affixquestion particleMorphNegweakly suffixingplural suffixno grammatical gender[@dataset_fa_sentipers]
44frfr_dai_laborsocial_mediaIndo-EuropeanRomancedefinite word distinct from demonstrativeindefinite word same as oneno morphological case-makingSVOnegative particlequestion particleOptDoubleNegstrongly suffixingplural suffixmasculine, feminine[@dataset_dai_labor]
45frfr_ifeelsocial_mediaIndo-EuropeanRomancedefinite word distinct from demonstrativeindefinite word same as oneno morphological case-makingSVOnegative particlequestion particleOptDoubleNegstrongly suffixingplural suffixmasculine, feminine[@dataset_dai_labor]
46frfr_multilan_amazonreviewsIndo-EuropeanRomancedefinite word distinct from demonstrativeindefinite word same as oneno morphological case-makingSVOnegative particlequestion particleOptDoubleNegstrongly suffixingplural suffixmasculine, feminine[@dataset_multilan_amazon]
47hehe_hebrew_sentimentsocial_mediaAfro-AsiaticSemiticdefinite affixindefinite word same as oneno morphological case-makingSVOnegative particlequestion particleSNegVOweakly suffixingplural suffixmasculine, feminine[@dataset_he_hebrew_sentiment]
48hihi_semeval2020social_mediaIndo-EuropeanIndicno articleno article3SOVnegative particlequestion particleSONegVstrongly suffixingplural suffixmasculine, feminine[@dataset_semeval_2020]
49hrhr_sentiment_news_documentnewsIndo-EuropeanSlavicno articleno article5SVOnegative particlequestion particleotherstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_hr_sentiment_news_document]
50hrhr_twitter_sentimentsocial_mediaIndo-EuropeanSlavicno articleno article5SVOnegative particlequestion particleotherstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_twitter_sentiment]
51huhu_twitter_sentimentsocial_mediaUralicUgricdefinite word distinct from demonstrativeindefinite word distinct from one10 or moreno dominant ordernegative particlequestion particleSNegVOstrongly suffixingplural suffixno grammatical gender[@dataset_twitter_sentiment]
52itit_evalita2016social_mediaIndo-EuropeanRomancedefinite word distinct from demonstrativeindefinite word same as oneno morphological case-makingSVOnegative particleinterrogative intonation onlySNegVOstrongly suffixingplural suffixmasculine, feminine[@dataset_it_evalita2016]
53itit_multiemotionssocial_mediaIndo-EuropeanRomancedefinite word distinct from demonstrativeindefinite word same as oneno morphological case-makingSVOnegative particleinterrogative intonation onlySNegVOstrongly suffixingplural suffixmasculine, feminine[@dataset_it_multiemotions]
54jaja_multilan_amazonreviewsJapaneseJapaneseno articleindefinite word distinct from one8-9SOVnegative affixquestion particleMorphNegstrongly suffixingplural suffixno grammatical gender[@dataset_multilan_amazon]
55lvlv_ltec_sentimentsocial_mediaIndo-EuropeanBalticdemonstrative word used as definite articleindefinite word same as one5SVOnegative affixquestion particleMorphNegweakly suffixingplural suffixmasculine, feminine[@dataset_lv_ltec_sentiment]
56plpl_twitter_sentimentsocial_mediaIndo-EuropeanSlavicno articleno article6-7SVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_twitter_sentiment]
57plpl_polemoreviewsIndo-EuropeanSlavicno articleno article6-7SVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_pl_polemo]
58plpl_klej_allegro_reviewsreviewsIndo-EuropeanSlavicno articleno article6-7SVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_pl_klej_allegro_reviews]
59plpl_opi_lil_2012social_mediaIndo-EuropeanSlavicno articleno article6-7SVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_pl_opi_lil_2012]
60ptpt_dai_laborsocial_mediaIndo-EuropeanRomancedefinite word distinct from demonstrativeindefinite word same as oneno morphological case-makingSVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine[@dataset_dai_labor]
61ptpt_ifeelsocial_mediaIndo-EuropeanRomancedefinite word distinct from demonstrativeindefinite word same as oneno morphological case-makingSVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine[@dataset_dai_labor]
62ptpt_tweet_sent_brsocial_mediaIndo-EuropeanRomancedefinite word distinct from demonstrativeindefinite word same as oneno morphological case-makingSVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine[@dataset_pt_tweet_sent_br]
63ptpt_twitter_sentimentsocial_mediaIndo-EuropeanRomancedefinite word distinct from demonstrativeindefinite word same as oneno morphological case-makingSVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine[@dataset_twitter_sentiment]
64ruru_sentimentsocial_mediaIndo-EuropeanSlavicno articleno article6-7SVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_ru_sentiment]
65ruru_twitter_sentimentsocial_mediaIndo-EuropeanSlavicno articleno article6-7SVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_twitter_sentiment]
66sksk_twitter_sentimentsocial_mediaIndo-EuropeanSlavicno articleno article6-7SVOnegative affixinterrogative word orderMorphNegweakly suffixingplural suffixmasculine, feminine, neuter[@dataset_twitter_sentiment]
67slsl_sentinewsnewsIndo-EuropeanSlavicno articleno article6-7SVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine, neuter[@Bučar2018]
68slsl_twitter_sentimentsocial_mediaIndo-EuropeanSlavicno articleno article6-7SVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_twitter_sentiment]
69sqsq_twitter_sentimentsocial_mediaIndo-EuropeanAlbaniandefinite affixindefinite word distinct from one4SVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine[@dataset_twitter_sentiment]
70srsr_movie_reviewsreviewsIndo-EuropeanSlavicno articleno article5SVOnegative particlequestion particleotherstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_sr_serb_movie_reviews]
71srsr_senticommentsreviewsIndo-EuropeanSlavicno articleno article5SVOnegative particlequestion particleotherstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_sr_senticomments]
72srsr_twitter_sentimentsocial_mediaIndo-EuropeanSlavicno articleno article5SVOnegative particlequestion particleotherstrongly suffixingplural suffixmasculine, feminine, neuter[@dataset_twitter_sentiment]
73svsv_twitter_sentimentsocial_mediaIndo-EuropeanGermanicdefinite affixindefinite word same as one2SVOnegative particleinterrogative word ordermore than one positionstrongly suffixingplural suffixcommon, neuter[@dataset_twitter_sentiment]
74thth_wongnai_reviewsreviewsTai-KadaiKam-Taino articleindefinite word distinct from oneno morphological case-makingSVOnegative auxiliary verbquestion particleSNegVOlittle affixationmixed morphological pluralnoun classifiers[@dataset_th_wongnai_reviews]
75thth_wisesight_sentimentsocial_mediaTai-KadaiKam-Taino articleindefinite word distinct from oneno morphological case-makingSVOnegative auxiliary verbquestion particleSNegVOlittle affixationmixed morphological pluralnoun classifiers[@dataset_th_wisesight_sentiment]
76urur_roman_urdumixedIndo-EuropeanIndicno articleno article2SOVnegative affixquestion particleSONegVstrongly suffixingplural suffixmasculine, feminine[@dataset_ur_roman_urdu]
77zhzh_hotel_reviewsreviewsSino-TibetanChineseno articleindefinite word same as oneno morphological case-makingSVOnegative particlequestion particleSNegVOlittle affixationno pluralnoun classifiers[@dataset_zh_hotel_reviews]
78zhzh_multilan_amazonreviewsSino-TibetanChineseno articleindefinite word same as oneno morphological case-makingSVOnegative particlequestion particleSNegVOlittle affixationno pluralnoun classifiers[@dataset_multilan_amazon]
+ +
+
+
+
+
+
+

Dataset Stats

+
+

Datasets per language

+
+
pd.DataFrame(mms_dataset_df.groupby("language").original_dataset.nunique().sort_values(ascending=False))
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
original_dataset
language
en17
ar9
de6
es5
pl4
cs4
pt4
sr3
fr3
th2
sl2
ru2
it2
hr2
zh2
bg1
ja1
lv1
hu1
hi1
sk1
he1
sq1
fa1
sv1
bs1
ur1
+ +
+
+
+
+
+

Labels per language

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+
pd.DataFrame(mms_dataset_df.groupby(by=["language", "label_name"]).count()["text"])
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
text
languagelabel_name
arnegative138899
neutral192774
positive600402
bgnegative13930
neutral28657
positive19563
bsnegative11974
neutral11145
positive13064
csnegative39674
neutral59200
positive97413
denegative104667
neutral100071
positive111149
ennegative304939
neutral290823
positive1734724
esnegative108733
neutral122493
positive187486
fanegative1602
neutral5091
positive6832
frnegative84187
neutral43245
positive83199
henegative2279
neutral243
positive6097
hinegative4992
neutral6392
positive5615
hrnegative19757
neutral19470
positive38367
hunegative8974
neutral17621
positive30087
itnegative4043
neutral4193
positive3829
janegative83982
neutral41979
positive83819
lvnegative1378
neutral2618
positive1794
plnegative77422
neutral62074
positive97192
ptnegative56827
neutral55165
positive45842
runegative31770
neutral48106
positive31054
sknegative14431
neutral12842
positive29350
slnegative33694
neutral50553
positive29296
sqnegative6889
neutral14757
positive22638
srnegative25089
neutral32283
positive18996
svnegative16266
neutral13342
positive11738
thnegative9326
neutral28616
positive34377
urnegative5239
neutral8585
positive5836
zhnegative117967
neutral69016
positive144719
+ +
+
+
+
+
+

Texts in Language Family and Genus

+
+
pd.DataFrame(mms_dataset_df.groupby(by=['Family', 'Genus',]).count()["text"])
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
text
FamilyGenus
Afro-AsiaticSemitic940694
Indo-EuropeanAlbanian44284
Baltic5790
Germanic2687719
Indic36659
Iranian13525
Romance799242
Slavic966366
JapaneseJapanese209780
Sino-TibetanChinese331702
Tai-KadaiKam-Tai72319
UralicUgric56682
+ +
+
+
+
+
+

Examples per domain

+
+
pd.DataFrame(mms_dataset_df.groupby(by=["domain"]).count()["text"])
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
text
domain
chats16781
mixed94122
news26413
poems1052
reviews4510893
social_media1515501
+ +
+
+
+
+
+
+

Hosting, Licensing, and Maintenance Plan

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    +
  • Hosting: The datasets and benchmark will be hosted on a reliable and scalable cloud infrastructure to ensure accessibility and availability (HuggingFace Hub). The choice of hosting platform will be based on factors such as reliability, performance, and cost-effectiveness.
    • +
  • Licensing: We will clearly state the data license under which the datasets are released, ensuring that the terms of use are explicitly defined. We will consider licenses that facilitate research and allow for derivative works, while also addressing potential ethical considerations. See the license in repository.
    • +
  • Maintenance: We (see Dataset Curators section) are committed to providing ongoing maintenance and support for the datasets and benchmark. This includes regular updates, bug fixes, and addressing any user feedback or inquiries. We will also establish a communication channel for users to report issues or request assistance.
    • +
+ + + +
+ +

References

+
+Northcutt, Curtis, Lu Jiang, and Isaac Chuang. 2021. “Confident Learning: Estimating Uncertainty in Dataset Labels.” Journal of Artificial Intelligence Research 70: 1373–1411. +
+
+ +
+ + + + \ No newline at end of file diff --git a/images/quality.png b/images/quality.png new file mode 100644 index 0000000..80789ee Binary files /dev/null and b/images/quality.png differ diff --git a/index.html b/index.html new file mode 100644 index 0000000..1b55ac6 --- /dev/null +++ b/index.html @@ -0,0 +1,1124 @@ + + + + + + + + + + +mms_benchmark - MMS Dataset and Benchmark + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+
+
+

MMS Dataset and Benchmark

+
+
+ The most extensive open massively multilingual corpus of datasets for training sentiment models. The corpus consists of 79 manually selected from over 350 datasets reported in the scientific literature based on strict quality criteria and covers 27 languages. +
+
+
+
+ + +
+ + + + +
+ + +
+ + +
+ + + +
+ + + + + +

Despite impressive advancements in multilingual corpora collection and model training, developing large-scale deployments of multilingual models still presents a significant challenge. This is particularly true for language tasks that are culture-dependent. One such example is the area of multilingual sentiment analysis, where affective markers can be subtle and deeply ensconced in culture.

+

This work presents the most extensive open massively multilingual corpus of datasets for training sentiment models. The corpus consists of 79 manually selected datasets from over 350 datasets reported in the scientific literature based on strict quality criteria. The corpus covers 27 languages representing 6 language families. Datasets can be queried using several linguistic and functional features. In addition, we present a multi-faceted sentiment classification benchmark summarizing hundreds of experiments conducted on different base models, training objectives, dataset collections, and fine-tuning strategies.

+
+

Dataset

+

Massively Multilingual Sentiment Datasets

+
+
+

Analysis and benchmarking

+

HuggingFace Spaces with Analysis and Benchmark

+
+
+

General statistics about the dataset

+
+

It may take some time to download the dataset and generate train set inside HuggingFace dataset. Please be patient.

+
+
+
mms_dataset = datasets.load_dataset("Brand24/mms")
+
+
+
mms_dataset_df = mms_dataset["train"].to_pandas()
+
+

How many examples do we have?

+
+
mms_dataset.num_rows
+
+
{'train': 6164762}
+
+
+
+
+

Features

+

We provide not only texts and sentiment labels but we assigned many additional dimensions for datasets and languages, hence it is possible to splice and dice them as you want and need.

+
+
mms_dataset["train"].features
+
+
{'_id': Value(dtype='int32', id=None),
+ 'text': Value(dtype='string', id=None),
+ 'label': ClassLabel(names=['negative', 'neutral', 'positive'], id=None),
+ 'original_dataset': Value(dtype='string', id=None),
+ 'domain': Value(dtype='string', id=None),
+ 'language': Value(dtype='string', id=None),
+ 'Family': Value(dtype='string', id=None),
+ 'Genus': Value(dtype='string', id=None),
+ 'Definite articles': Value(dtype='string', id=None),
+ 'Indefinite articles': Value(dtype='string', id=None),
+ 'Number of cases': Value(dtype='string', id=None),
+ 'Order of subject, object, verb': Value(dtype='string', id=None),
+ 'Negative morphemes': Value(dtype='string', id=None),
+ 'Polar questions': Value(dtype='string', id=None),
+ 'Position of negative word wrt SOV': Value(dtype='string', id=None),
+ 'Prefixing vs suffixing': Value(dtype='string', id=None),
+ 'Coding of nominal plurality': Value(dtype='string', id=None),
+ 'Grammatical genders': Value(dtype='string', id=None),
+ 'cleanlab_self_confidence': Value(dtype='float32', id=None)}
+
+
+
+

Example

+
+
mms_dataset["train"][2001000]
+
+
{'_id': 2001000,
+ 'text': 'I was a tomboy and this has such great memories for me. They fit exactly how I remember, PERFECTLY!!',
+ 'label': 2,
+ 'original_dataset': 'en_amazon',
+ 'domain': 'reviews',
+ 'language': 'en',
+ 'Family': 'Indo-European',
+ 'Genus': 'Germanic',
+ 'Definite articles': 'definite word distinct from demonstrative',
+ 'Indefinite articles': 'indefinite word distinct from one',
+ 'Number of cases': '2',
+ 'Order of subject, object, verb': 'SVO',
+ 'Negative morphemes': 'negative particle',
+ 'Polar questions': 'interrogative word order',
+ 'Position of negative word wrt SOV': 'SNegVO',
+ 'Prefixing vs suffixing': 'strongly suffixing',
+ 'Coding of nominal plurality': 'plural suffix',
+ 'Grammatical genders': 'no grammatical gender',
+ 'cleanlab_self_confidence': 0.9978116750717163}
+
+
+
+
+

Classes

+
+
labels = mms_dataset["train"].features["label"].names
+labels
+
+
['negative', 'neutral', 'positive']
+
+
+
+
mms_dataset_df["label_name"] = mms_dataset_df["label"].apply(lambda x: labels[x])
+
+
+
+

Classes distribution

+
+
labels_stats_df = pd.DataFrame(mms_dataset_df.label_name.value_counts())
+labels_stats_df["percentage"] = (labels_stats_df["label_name"] / labels_stats_df["label_name"].sum()).round(3)
+labels_stats_df
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
label_namepercentage
positive34944780.567
neutral13413540.218
negative13289300.216
+ +
+
+
+
+
+
+

Sentiment orientation for each language

+
+
cols = ['language', 'label_name']
+mms_dataset_df[cols].value_counts().to_frame().reset_index().rename(columns={0: 'count'}).sort_values(by=cols, ascending=True)
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
languagelabel_namecount
7arnegative138899
4arneutral192774
1arpositive600402
53bgnegative13930
41bgneutral28657
............
62urneutral8585
67urpositive5836
9zhnegative117967
21zhneutral69016
6zhpositive144719
+ +

81 rows × 3 columns

+
+
+
+
+
+

Per language

+
+
cols = ['language']
+mms_dataset_df[cols].value_counts().to_frame().reset_index().rename(columns={0: 'count'}).sort_values(by=cols, ascending=True)
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
languagecount
1ar932075
15bg62150
20bs36183
8cs196287
4de315887
0en2330486
2es418712
23fa13525
6fr210631
25he8619
22hi16999
12hr77594
16hu56682
24it12065
7ja209780
26lv5790
5pl236688
9pt157834
11ru110930
17sk56623
10sl113543
18sq44284
13sr76368
19sv41346
14th72319
21ur19660
3zh331702
+ +
+
+
+
+
+

Example of filtering datasets

+
+

Choose only Polish

+
+
pl = mms_dataset.filter(lambda row: row['language'] == 'pl')
+
+ +
+
+
+
pl["train"].to_pandas().sample(5)
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
_idtextlabeloriginal_datasetdomainlanguageFamilyGenusDefinite articlesIndefinite articlesNumber of casesOrder of subject, object, verbNegative morphemesPolar questionsPosition of negative word wrt SOVPrefixing vs suffixingCoding of nominal pluralityGrammatical genderscleanlab_self_confidence
2159215119386Typujcie jaki dziś będzie wynik St.Pats - Legi...2pl_twitter_sentimentsocial_mediaplIndo-EuropeanSlavicno articleno article6-7SVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine, neuter0.589098
865254989990@KaczmarSF Przyjemne ciarki mam, gdy patrzę na...2pl_twitter_sentimentsocial_mediaplIndo-EuropeanSlavicno articleno article6-7SVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine, neuter0.950756
660314969496szkoda bylo czasu i kasy .0pl_polemoreviewsplIndo-EuropeanSlavicno articleno article6-7SVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine, neuter0.940540
1377685041233@shinyvalentine mam ja w dupie lecz bylo to kr...0pl_twitter_sentimentsocial_mediaplIndo-EuropeanSlavicno articleno article6-7SVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine, neuter0.220028
1187665022231@itiNieWracaj pokazują to gdzieś?2pl_twitter_sentimentsocial_mediaplIndo-EuropeanSlavicno articleno article6-7SVOnegative particlequestion particleSNegVOstrongly suffixingplural suffixmasculine, feminine, neuter0.139179
+ +
+
+
+
+
+
+

Use cases

+
+

Case 1

+

Thus, when training a sentiment classifier using our dataset, one may download different facets of the collection. For instance, one can download all datasets in Slavic languages in which polar questions are formed using the interrogative word order or download all datasets from the Afro-Asiatic language family with no morphological case-making.

+
+
slavic = mms_dataset.filter(lambda row: row["Genus"] == "Slavic" and row["Polar questions"] == "interrogative word order")
+
+ +
+
+
+
slavic
+
+
DatasetDict({
+    train: Dataset({
+        features: ['_id', 'text', 'label', 'original_dataset', 'domain', 'language', 'Family', 'Genus', 'Definite articles', 'Indefinite articles', 'Number of cases', 'Order of subject, object, verb', 'Negative morphemes', 'Polar questions', 'Position of negative word wrt SOV', 'Prefixing vs suffixing', 'Coding of nominal plurality', 'Grammatical genders', 'cleanlab_self_confidence'],
+        num_rows: 252910
+    })
+})
+
+
+
+
+

Case 2

+
+
afro_asiatic = mms_dataset.filter(lambda row: row["Family"] == "Afro-Asiatic" and row["Number of cases"] == "no morphological case-making")
+
+ +
+
+
+
afro_asiatic
+
+
DatasetDict({
+    train: Dataset({
+        features: ['_id', 'text', 'label', 'original_dataset', 'domain', 'language', 'Family', 'Genus', 'Definite articles', 'Indefinite articles', 'Number of cases', 'Order of subject, object, verb', 'Negative morphemes', 'Polar questions', 'Position of negative word wrt SOV', 'Prefixing vs suffixing', 'Coding of nominal plurality', 'Grammatical genders', 'cleanlab_self_confidence'],
+        num_rows: 8619
+    })
+})
+
+
+
+
+
+

Dataset Curators

+

The corpus was put together by

+ +
+
+

Citation

+
@misc{augustyniak2023massively,
+      title={Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmark}, 
+      author={Łukasz Augustyniak and Szymon Woźniak and Marcin Gruza and Piotr Gramacki and Krzysztof Rajda and Mikołaj Morzy and Tomasz Kajdanowicz},
+      year={2023},
+      eprint={2306.07902},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+
+
+

Acknowledgements

+
    +
  • BRAND24 - https://brand24.com
    • +
  • CLARIN-PL-Biz - https://clarin.biz
    • +
+
+
+

Licensing Information

+

These data are released under this licensing scheme. We do not own any text from which these data and datasets have been extracted.

+

We license the actual packaging of these data under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommons.org/licenses/by-nc/4.0/

+

This work is published from Poland.

+

Should you consider that our data contains material that is owned by you and should, therefore not be reproduced here, please: * Clearly identify yourself with detailed contact data such as an address, telephone number, or email address at which you can be contacted. * Clearly identify the copyrighted work claimed to be infringed. * Clearly identify the material claimed to be infringing and the information reasonably sufficient to allow us to locate the material.

+

We will comply with legitimate requests by removing the affected sources from the next release of the corpus.

+ + +
+ +
+ + +
+ + + + \ No newline at end of file diff --git a/robots.txt b/robots.txt new file mode 100644 index 0000000..a915b08 --- /dev/null +++ b/robots.txt @@ -0,0 +1 @@ +Sitemap: https://Brand24-AI.github.io/mms_benchmark/mms_benchmark/sitemap.xml diff --git a/search.json b/search.json new file mode 100644 index 0000000..0e24b44 --- /dev/null +++ b/search.json @@ -0,0 +1,177 @@ +[ + { + "objectID": "training_example.html", + "href": "training_example.html", + "title": "MMS - Example training pipeline", + "section": "", + "text": "!pip install datasets transformers==4.30.0 torch sacremoses scikit-learn evaluate accelerate\n\n\nimport os\n\nimport evaluate\nimport numpy as np\nfrom datasets import load_dataset\nfrom transformers import (\n AutoModelForSequenceClassification,\n AutoTokenizer,\n Trainer,\n TrainingArguments,\n)\n\nOur dataset is publicly available but we need to you to accept conditions. Please see this link, accept the terms\n\nmms_dataset = load_dataset(\"Brand24/mms\")\n\nDownloading and preparing dataset mms/default to /root/.cache/huggingface/datasets/Brand24___mms/default/0.2.0/70532fdd01f149ff84a280b7d9cfb661643abf4837b4f0f3aa1128064e870d65...\nDataset mms downloaded and prepared to /root/.cache/huggingface/datasets/Brand24___mms/default/0.2.0/70532fdd01f149ff84a280b7d9cfb661643abf4837b4f0f3aa1128064e870d65. Subsequent calls will reuse this data.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nThere are 14 different dimensions which differentiate obtained datasets. In addition, there is a pre-calculated cleanlab self conficence score for each sample. All of them can be used to sample examples which suit our use case best\n\nmms_dataset.column_names\n\n{'train': ['_id',\n 'text',\n 'label',\n 'original_dataset',\n 'domain',\n 'language',\n 'Family',\n 'Genus',\n 'Definite articles',\n 'Indefinite articles',\n 'Number of cases',\n 'Order of subject, object, verb',\n 'Negative morphemes',\n 'Polar questions',\n 'Position of negative word wrt SOV',\n 'Prefixing vs suffixing',\n 'Coding of nominal plurality',\n 'Grammatical genders',\n 'cleanlab_self_confidence']}\n\n\nSelect only samples in polish and coming from social media\n\npl_sm = mms_dataset[\"train\"].filter(lambda x: x[\"language\"] == \"pl\" and x[\"domain\"] == \"social_media\")\n\n\n\n\nTo achieve higher performance, we will select only samples with high self confidence score\n\npl_sm_high_confidence = pl_sm.filter(lambda x: x[\"cleanlab_self_confidence\"] > 0.6)\n\n\n\n\n\nlen(pl_sm_high_confidence)\n\n73227\n\n\nWe will use this examples to fine-tune Polish version of BERT model - HerBERT\n\ntokenizer = AutoTokenizer.from_pretrained(\"allegro/herbert-base-cased\")\n\ndef tokenize(batch):\n return tokenizer(batch[\"text\"], padding=\"max_length\", truncation=True)\n\n\ntokenized_dataset = pl_sm_high_confidence.map(tokenize, batched=True, batch_size=512)\n\n\n\n\n\nmodel = AutoModelForSequenceClassification.from_pretrained(\"allegro/herbert-base-cased\", num_labels=3)\n\nSome weights of the model checkpoint at allegro/herbert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.sso.sso_relationship.weight', 'cls.predictions.decoder.bias', 'cls.sso.sso_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias']\n- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\nSome weights of BertForSequenceClassification were not initialized from the model checkpoint at allegro/herbert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n\n\n\nsplit_dataset = tokenized_dataset.train_test_split(test_size=0.1)\ntrain_dataset = split_dataset[\"train\"]\neval_dataset = split_dataset[\"test\"]\n\n\ntraining_args = TrainingArguments(\n output_dir=\"PL_SM_SENT\",\n evaluation_strategy=\"epoch\",\n num_train_epochs=1,\n)\nmetric = evaluate.load(\"accuracy\")\n\n\ndef compute_metrics(eval_pred):\n logits, labels = eval_pred\n predictions = np.argmax(logits, axis=-1)\n return metric.compute(predictions=predictions, references=labels)\n\n\ntrainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=train_dataset,\n eval_dataset=eval_dataset,\n compute_metrics=compute_metrics,\n)\n\n\ntrainer.train()\n\n/opt/conda/lib/python3.10/site-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n warnings.warn(" + }, + { + "objectID": "citations.html", + "href": "citations.html", + "title": "MMS Dataset Citations", + "section": "", + "text": "Domain: social_media\nLanguage: ar\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: masculine, feminine\n\n@InProceedings{dataset_ar_arsentdl,\n author = {Ramy Baly and\n Alaa Khaddaj and\n Hazem M. Hajj and\n Wassim El{-}Hajj and\n Khaled Bashir Shaban},\n title = {{ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets}},\n booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},\n year = {2018},\n month = {may},\n date = {7-12},\n location = {Miyazaki, Japan},\n editor = {Hend Al-Khalifa and King Saud University and KSA Walid Magdy and University of Edinburgh and UK Kareem Darwish and Qatar Computing Research Institute and Qatar Tamer Elsayed and Qatar University and Qatar},\n publisher = {European Language Resources Association (ELRA)},\n address = {Paris, France},\n isbn = {979-10-95546-25-2},\n language = {english}\n}\n\n\n\n\n\nDomain: social_media\nLanguage: ar\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_ar_astd,\n title = \"{ASTD}: {A}rabic Sentiment Tweets Dataset\",\n author = \"Nabil, Mahmoud and\n Aly, Mohamed and\n Atiya, Amir\",\n booktitle = \"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing\",\n month = sep,\n year = \"2015\",\n address = \"Lisbon, Portugal\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/D15-1299\",\n doi = \"10.18653/v1/D15-1299\",\n pages = \"2515--2519\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: ar\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_ar_bbn,\n title = \"Sentiment after Translation: A Case-Study on {A}rabic Social Media Posts\",\n author = \"Salameh, Mohammad and\n Mohammad, Saif and\n Kiritchenko, Svetlana\",\n booktitle = \"Proceedings of the 2015 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies\",\n month = may # \"{--}\" # jun,\n year = \"2015\",\n address = \"Denver, Colorado\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/N15-1078\",\n doi = \"10.3115/v1/N15-1078\",\n pages = \"767--777\",\n}\n\n\n\n\n\nDomain: reviews\nLanguage: ar\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: masculine, feminine\n\n@INPROCEEDINGS{dataset_ar_brad,\n author={Elnagar, Ashraf and Einea, Omar},\n booktitle={2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)}, \n title={{BRAD} 1.0: Book reviews in Arabic dataset}, \n year={2016},\n volume={},\n number={},\n pages={1-8},\n doi={10.1109/AICCSA.2016.7945800}\n}\n\n\n\n\n\nDomain: reviews\nLanguage: ar\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: masculine, feminine\n\n@Book{dataset_ar_hard,\n author=\"Elnagar, Ashraf\n and Khalifa, Yasmin S.\n and Einea, Anas\",\n title={Hotel Arabic-Reviews Dataset Construction for Sentiment Analysis Applications},\n bookTitle=\"Intelligent Natural Language Processing: Trends and Applications\",\n year=\"2018\",\n publisher=\"Springer International Publishing\",\n address=\"Cham\",\n pages=\"35--52\",\n isbn=\"978-3-319-67056-0\",\n doi=\"10.1007/978-3-319-67056-0_3\",\n url=\"https://doi.org/10.1007/978-3-319-67056-0_3\"\n}\n\n\n\n\n\nDomain: reviews\nLanguage: ar\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_ar_labr,\n title = \"{LABR}: A Large Scale {A}rabic Book Reviews Dataset\",\n author = \"Aly, Mohamed and\n Atiya, Amir\",\n booktitle = \"Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)\",\n month = aug,\n year = \"2013\",\n address = \"Sofia, Bulgaria\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/P13-2088\",\n pages = \"494--498\",\n}\n\n\n\n\n\nDomain: reviews\nLanguage: ar\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_ar_oclar,\n author={Al Omari, Marwan and Al-Hajj, Moustafa and Hammami, Nacereddine and Sabra, Amani},\n booktitle={2019 International Conference on Computer and Information Sciences (ICCIS)}, \n title={Sentiment Classifier: Logistic Regression for Arabic Services’ Reviews in Lebanon}, \n year={2019},\n volume={},\n number={},\n pages={1-5},\n doi={10.1109/ICCISci.2019.8716394}\n}\n\n\n\n\n\nDomain: mixed\nLanguage: ar\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_semeval_2017,\n title = \"{S}em{E}val-2017 Task 4: Sentiment Analysis in {T}witter\",\n author = \"Rosenthal, Sara and\n Farra, Noura and\n Nakov, Preslav\",\n booktitle = \"Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)\",\n month = aug,\n year = \"2017\",\n address = \"Vancouver, Canada\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/S17-2088\",\n doi = \"10.18653/v1/S17-2088\",\n pages = \"502--518\",\n abstract = \"This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: ar\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_ar_bbn,\n title = \"Sentiment after Translation: A Case-Study on {A}rabic Social Media Posts\",\n author = \"Salameh, Mohammad and\n Mohammad, Saif and\n Kiritchenko, Svetlana\",\n booktitle = \"Proceedings of the 2015 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies\",\n month = may # \"{--}\" # jun,\n year = \"2015\",\n address = \"Denver, Colorado\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/N15-1078\",\n doi = \"10.3115/v1/N15-1078\",\n pages = \"767--777\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: bg\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: no article\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: bs\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 5\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: other\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: cs\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative affix\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: MorphNeg\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_cs_social_media,\n title = \"Sentiment Analysis in {C}zech Social Media Using Supervised Machine Learning\",\n author = \"Habernal, Ivan and\n Pt{\\'a}{\\v{c}}ek, Tom{\\'a}{\\v{s}} and\n Steinberger, Josef\",\n booktitle = \"Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis\",\n month = jun,\n year = \"2013\",\n address = \"Atlanta, Georgia\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/W13-1609\",\n pages = \"65--74\",\n}\n\n\n\n\n\nDomain: reviews\nLanguage: cs\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative affix\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: MorphNeg\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_cs_social_media,\n title = \"Sentiment Analysis in {C}zech Social Media Using Supervised Machine Learning\",\n author = \"Habernal, Ivan and\n Pt{\\'a}{\\v{c}}ek, Tom{\\'a}{\\v{s}} and\n Steinberger, Josef\",\n booktitle = \"Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis\",\n month = jun,\n year = \"2013\",\n address = \"Atlanta, Georgia\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/W13-1609\",\n pages = \"65--74\",\n}\n\n\n\n\n\nDomain: reviews\nLanguage: cs\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative affix\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: MorphNeg\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_cs_social_media,\n title = \"Sentiment Analysis in {C}zech Social Media Using Supervised Machine Learning\",\n author = \"Habernal, Ivan and\n Pt{\\'a}{\\v{c}}ek, Tom{\\'a}{\\v{s}} and\n Steinberger, Josef\",\n booktitle = \"Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis\",\n month = jun,\n year = \"2013\",\n address = \"Atlanta, Georgia\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/W13-1609\",\n pages = \"65--74\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: cs\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative affix\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: MorphNeg\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_cs_social_media,\n title = \"Sentiment Analysis in {C}zech Social Media Using Supervised Machine Learning\",\n author = \"Habernal, Ivan and\n Pt{\\'a}{\\v{c}}ek, Tom{\\'a}{\\v{s}} and\n Steinberger, Josef\",\n booktitle = \"Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis\",\n month = jun,\n year = \"2013\",\n address = \"Atlanta, Georgia\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/W13-1609\",\n pages = \"65--74\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: de\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: 4\nOrder of subject, object, verb: no dominant order\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: more than one position\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_dai_labor,\n author = {Narr, Sascha and Michael Hülfenhaus and Albayrak, Sahin},\n title = {Language-Independent Twitter Sentiment Analysis},\n booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},\n year = {2012},\n location = {Dortmund, Germany},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: de\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: 4\nOrder of subject, object, verb: no dominant order\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: more than one position\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_dai_labor,\n author = {Narr, Sascha and Michael Hülfenhaus and Albayrak, Sahin},\n title = {Language-Independent Twitter Sentiment Analysis},\n booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},\n year = {2012},\n location = {Dortmund, Germany},\n}\n\n\n\n\n\nDomain: reviews\nLanguage: de\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: 4\nOrder of subject, object, verb: no dominant order\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: more than one position\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_multilan_amazon,\n title = \"The Multilingual {A}mazon Reviews Corpus\",\n author = {Keung, Phillip and\n Lu, Yichao and\n Szarvas, Gy{\\\"o}rgy and\n Smith, Noah A.},\n booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.emnlp-main.369\",\n doi = \"10.18653/v1/2020.emnlp-main.369\",\n pages = \"4563--4568\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: de\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: 4\nOrder of subject, object, verb: no dominant order\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: more than one position\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_de_omp,\n title = \"Academic-Industrial Perspective on the Development and Deployment of a Moderation System for a Newspaper Website\",\n author = \"Schabus, Dietmar and\n Skowron, Marcin\",\n booktitle = \"Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)\",\n month = may,\n year = \"2018\",\n address = \"Miyazaki, Japan\",\n publisher = \"European Language Resources Association (ELRA)\",\n url = \"https://aclanthology.org/L18-1253\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: de\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: 4\nOrder of subject, object, verb: no dominant order\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: more than one position\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_de_sb10k,\n title = \"A {T}witter Corpus and Benchmark Resources for {G}erman Sentiment Analysis\",\n author = \"Cieliebak, Mark and\n Deriu, Jan Milan and\n Egger, Dominic and\n Uzdilli, Fatih\",\n booktitle = \"Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media\",\n month = apr,\n year = \"2017\",\n address = \"Valencia, Spain\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/W17-1106\",\n doi = \"10.18653/v1/W17-1106\",\n pages = \"45--51\",\n abstract = \"In this paper we present SB10k, a new corpus for sentiment analysis with approx. 10,000 German tweets. We use this new corpus and two existing corpora to provide state-of-the-art benchmarks for sentiment analysis in German: we implemented a CNN (based on the winning system of SemEval-2016) and a feature-based SVM and compare their performance on all three corpora. For the CNN, we also created German word embeddings trained on 300M tweets. These word embeddings were then optimized for sentiment analysis using distant-supervised learning. The new corpus, the German word embeddings (plain and optimized), and source code to re-run the benchmarks are publicly available.\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: de\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: 4\nOrder of subject, object, verb: no dominant order\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: more than one position\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: reviews\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_en_amazon,\n title = \"Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects\",\n author = \"Ni, Jianmo and\n Li, Jiacheng and\n McAuley, Julian\",\n booktitle = \"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)\",\n month = nov,\n year = \"2019\",\n address = \"Hong Kong, China\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/D19-1018\",\n doi = \"10.18653/v1/D19-1018\",\n pages = \"188--197\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_dai_labor,\n author = {Narr, Sascha and Michael Hülfenhaus and Albayrak, Sahin},\n title = {Language-Independent Twitter Sentiment Analysis},\n booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},\n year = {2012},\n location = {Dortmund, Germany},\n}\n\n\n\n\n\nDomain: news\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@article{dataset_en_financial_phrasebank_sentences_75agree,\n author = {Malo, Pekka and Sinha, Ankur and Korhonen, Pekka and Wallenius, Jyrki and Takala, Pyry},\n title = {Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts},\n year = {2014},\n issue_date = {April 2014},\n publisher = {John Wiley & Sons, Inc.},\n address = {USA},\n volume = {65},\n number = {4},\n issn = {2330-1635},\n url = {https://doi.org/10.1002/asi.23062},\n doi = {10.1002/asi.23062},\n journal = {Journal of the Association for Information Science and Technology},\n month = {apr},\n pages = {782–796},\n numpages = {15},\n keywords = {economics, automatic classification, linguistic analysis}\n}\n\n\n\n\n\nDomain: reviews\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_multilan_amazon,\n title = \"The Multilingual {A}mazon Reviews Corpus\",\n author = {Keung, Phillip and\n Lu, Yichao and\n Szarvas, Gy{\\\"o}rgy and\n Smith, Noah A.},\n booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.emnlp-main.369\",\n doi = \"10.18653/v1/2020.emnlp-main.369\",\n pages = \"4563--4568\",\n}\n\n\n\n\n\nDomain: news\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_en_per_sent,\n title = \"Author{'}s Sentiment Prediction\",\n author = \"Bastan, Mohaddeseh and\n Koupaee, Mahnaz and\n Son, Youngseo and\n Sicoli, Richard and\n Balasubramanian, Niranjan\",\n booktitle = \"Proceedings of the 28th International Conference on Computational Linguistics\",\n month = dec,\n year = \"2020\",\n address = \"Barcelona, Spain (Online)\",\n publisher = \"International Committee on Computational Linguistics\",\n url = \"https://aclanthology.org/2020.coling-main.52\",\n doi = \"10.18653/v1/2020.coling-main.52\",\n pages = \"604--615\",\n}\n\n\n\n\n\nDomain: poems\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_en_poem_sentiment,\n title = \"Investigating Societal Biases in a Poetry Composition System\",\n author = \"Sheng, Emily and\n Uthus, David\",\n booktitle = \"Proceedings of the Second Workshop on Gender Bias in Natural Language Processing\",\n month = dec,\n year = \"2020\",\n address = \"Barcelona, Spain (Online)\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.gebnlp-1.9\",\n pages = \"93--106\",\n}\n\n\n\n\n\nDomain: mixed\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_semeval_2017,\n title = \"{S}em{E}val-2017 Task 4: Sentiment Analysis in {T}witter\",\n author = \"Rosenthal, Sara and\n Farra, Noura and\n Nakov, Preslav\",\n booktitle = \"Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)\",\n month = aug,\n year = \"2017\",\n address = \"Vancouver, Canada\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/S17-2088\",\n doi = \"10.18653/v1/S17-2088\",\n pages = \"502--518\",\n abstract = \"This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@article{dataset_en_sentistrength,\n author = {Thelwall, Mike and Buckley, Kevan and Paltoglou, Georgios},\n title = {Sentiment Strength Detection for the Social Web},\n year = {2012},\n issue_date = {January 2012},\n publisher = {John Wiley \\& Sons, Inc.},\n address = {USA},\n volume = {63},\n number = {1},\n issn = {1532-2882},\n url = {https://doi.org/10.1002/asi.21662},\n doi = {10.1002/asi.21662},\n abstract = {Sentiment analysis is concerned with the automatic extraction of sentiment-related\n information from text. Although most sentiment analysis addresses commercial tass,\n such as extracting opinions from product reviews, there is increasing interest in\n the affective dimension of the social web, and Twitter in particular. Most sentiment\n analysis algorithms are not ideally suited to this task because they exploit indirect\n indicators of sentiment that can reflect genre or topic instead. Hence, such algorithms\n used to process social web texts can identify spurious sentiment patterns caused by\n topics rather than affective phenomena. This article assesses an improved version\n of the algorithm SentiStrength for sentiment strength detection across the social\n web that primarily uses direct indications of sentiment. The results from six diverse\n social web data sets (MySpace, Twitter, YouTube, Digg, RunnersWorld, BBCForums) indicate\n that SentiStrength 2 is successful in the sense of performing better than a baseline\n approach for all data sets in both supervised and unsupervised cases. SentiStrength\n is not always better than machine-learning approaches that exploit indirect indicators\n of sentiment, however, and is particularly weaker for positive sentiment in news-related\n discussions. Overall, the results suggest that, even unsupervised, SentiStrength is\n robust enough to be applied to a wide variety of different social web contexts.},\n journal = {J. Am. Soc. Inf. Sci. Technol.},\n month = jan,\n pages = {163–173},\n numpages = {11}\n}\n\n\n\n\n\nDomain: chats\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_en_silicone,\n title = \"Hierarchical Pre-training for Sequence Labelling in Spoken Dialog\",\n author = \"Chapuis, Emile and\n Colombo, Pierre and\n Manica, Matteo and\n Labeau, Matthieu and\n Clavel, Chlo{\\'e}\",\n booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2020\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.findings-emnlp.239\",\n doi = \"10.18653/v1/2020.findings-emnlp.239\",\n pages = \"2636--2648\",\n}\n\n\n\n\n\nDomain: chats\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_en_silicone,\n title = \"Hierarchical Pre-training for Sequence Labelling in Spoken Dialog\",\n author = \"Chapuis, Emile and\n Colombo, Pierre and\n Manica, Matteo and\n Labeau, Matthieu and\n Clavel, Chlo{\\'e}\",\n booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2020\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.findings-emnlp.239\",\n doi = \"10.18653/v1/2020.findings-emnlp.239\",\n pages = \"2636--2648\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@misc{dataset_en_tweet_airlines,\n url={https://www.kaggle.com/crowdflower/twitter-airline-sentiment},\n author={Crowdflower Inc.},\n title={Twitter US Airline Sentiment},\n year={2015}\n}\n\n\n\n\n\nDomain: social_media\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@article{dataset_en_tweets_sanders,\n title={{Sanders-Twitter Sentiment Corpus}},\n author={Sanders, Niek J},\n journal={Sanders Analytics LLC},\n year={2011}\n}\n\n\n\n\n\nDomain: social_media\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: reviews\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_en_vader,\n title={{VADER}: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text},\n author={Clayton J. Hutto and Eric Gilbert},\n booktitle={Proceedings of the International AAAI Conference on Web and Social Media},\n year={2014},\n url={https://ojs.aaai.org/index.php/ICWSM/article/view/14550},\n month={May}, \n pages={216-225},\n volume=8,\n}\n\n\n\n\n\nDomain: reviews\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_en_vader,\n title={{VADER}: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text},\n author={Clayton J. Hutto and Eric Gilbert},\n booktitle={Proceedings of the International AAAI Conference on Web and Social Media},\n year={2014},\n url={https://ojs.aaai.org/index.php/ICWSM/article/view/14550},\n month={May}, \n pages={216-225},\n volume=8,\n}\n\n\n\n\n\nDomain: news\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_en_vader,\n title={{VADER}: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text},\n author={Clayton J. Hutto and Eric Gilbert},\n booktitle={Proceedings of the International AAAI Conference on Web and Social Media},\n year={2014},\n url={https://ojs.aaai.org/index.php/ICWSM/article/view/14550},\n month={May}, \n pages={216-225},\n volume=8,\n}\n\n\n\n\n\nDomain: social_media\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_en_vader,\n title={{VADER}: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text},\n author={Clayton J. Hutto and Eric Gilbert},\n booktitle={Proceedings of the International AAAI Conference on Web and Social Media},\n year={2014},\n url={https://ojs.aaai.org/index.php/ICWSM/article/view/14550},\n month={May}, \n pages={216-225},\n volume=8,\n}\n\n\n\n\n\nDomain: reviews\nLanguage: es\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@article{dataset_es_muchocine,\n title={Experiments in sentiment classification of movie reviews in Spanish},\n author={Cruz, Fermin L and Troyano, Jose A and Enriquez, Fernando and Ortega, Javier},\n journal={Procesamiento del Lenguaje Natural},\n volume={41},\n pages={73--80},\n year={2008},\n publisher={SOC ESPANOLA PROCESAMIENTO LENGUAJE NATURAL-SEPLN DEPT LENGUAJES \\& SISTEMAS~…}\n}\n\n\n\n\n\nDomain: reviews\nLanguage: es\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_multilan_amazon,\n title = \"The Multilingual {A}mazon Reviews Corpus\",\n author = {Keung, Phillip and\n Lu, Yichao and\n Szarvas, Gy{\\\"o}rgy and\n Smith, Noah A.},\n booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.emnlp-main.369\",\n doi = \"10.18653/v1/2020.emnlp-main.369\",\n pages = \"4563--4568\",\n}\n\n\n\n\n\nDomain: reviews\nLanguage: es\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@article{dataset_es_paper_reviews,\n author = {Keith Norambuena, Brian and Lettura, Exequiel and Villegas, Claudio},\n year = {2019},\n month = {02},\n pages = {191-214},\n title = {Sentiment analysis and opinion mining applied to scientific paper reviews},\n volume = {23},\n journal = {Intelligent Data Analysis},\n doi = {10.3233/IDA-173807}\n}\n\n\n\n\n\nDomain: social_media\nLanguage: es\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_semeval_2020,\n title = \"{S}em{E}val-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets\",\n author = {Patwa, Parth and\n Aguilar, Gustavo and\n Kar, Sudipta and\n Pandey, Suraj and\n PYKL, Srinivas and\n Gamb{\\\"a}ck, Bj{\\\"o}rn and\n Chakraborty, Tanmoy and\n Solorio, Thamar and\n Das, Amitava},\n booktitle = \"Proceedings of the Fourteenth Workshop on Semantic Evaluation\",\n month = dec,\n year = \"2020\",\n address = \"Barcelona (online)\",\n publisher = \"International Committee for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.semeval-1.100\",\n doi = \"10.18653/v1/2020.semeval-1.100\",\n pages = \"774--790\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: es\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: reviews\nLanguage: fa\nLanguage family: Indo-European\nGenus: Iranian\nDefinite articles: no article\nIndefinite articles: indefinite word same as one\nNumber of cases: 2\nOrder of subject, object, verb: SOV\nNegative morphemes: negative affix\nPolar questions: question particle\nPosition of negative word wrt SOV: MorphNeg\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@article{dataset_fa_sentipers,\n author = {Pedram Hosseini and\n Ali Ahmadian Ramaki and\n Hassan Maleki and\n Mansoureh Anvari and\n Seyed Abolghasem Mirroshandel},\n title = {{SentiPers}: {A} Sentiment Analysis Corpus for Persian},\n journal = {Computing Research Repository},\n volume = {arXiv:1801.07737},\n note = {Version 2},\n year = {2018},\n url = {http://arxiv.org/abs/1801.07737},\n eprinttype = {arXiv},\n eprint = {1801.07737},\n timestamp = {Mon, 13 Aug 2018 16:47:47 +0200},\n biburl = {https://dblp.org/rec/journals/corr/abs-1801-07737.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n\n\n\n\nDomain: social_media\nLanguage: fr\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: OptDoubleNeg\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_dai_labor,\n author = {Narr, Sascha and Michael Hülfenhaus and Albayrak, Sahin},\n title = {Language-Independent Twitter Sentiment Analysis},\n booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},\n year = {2012},\n location = {Dortmund, Germany},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: fr\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: OptDoubleNeg\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_dai_labor,\n author = {Narr, Sascha and Michael Hülfenhaus and Albayrak, Sahin},\n title = {Language-Independent Twitter Sentiment Analysis},\n booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},\n year = {2012},\n location = {Dortmund, Germany},\n}\n\n\n\n\n\nDomain: reviews\nLanguage: fr\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: OptDoubleNeg\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_multilan_amazon,\n title = \"The Multilingual {A}mazon Reviews Corpus\",\n author = {Keung, Phillip and\n Lu, Yichao and\n Szarvas, Gy{\\\"o}rgy and\n Smith, Noah A.},\n booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.emnlp-main.369\",\n doi = \"10.18653/v1/2020.emnlp-main.369\",\n pages = \"4563--4568\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: he\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_he_hebrew_sentiment,\n title = \"Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from {M}odern {H}ebrew\",\n author = \"Amram, Adam and\n Ben David, Anat and\n Tsarfaty, Reut\",\n booktitle = \"Proceedings of the 27th International Conference on Computational Linguistics\",\n month = aug,\n year = \"2018\",\n address = \"Santa Fe, New Mexico, USA\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/C18-1190\",\n pages = \"2242--2252\",\n abstract = \"This paper empirically studies the effects of representation choices on neural sentiment analysis for Modern Hebrew, a morphologically rich language (MRL) for which no sentiment analyzer currently exists. We study two dimensions of representational choices: (i) the granularity of the input signal (token-based vs. morpheme-based), and (ii) the level of encoding of vocabulary items (string-based vs. character-based). We hypothesise that for MRLs, languages where multiple meaning-bearing elements may be carried by a single space-delimited token, these choices will have measurable effects on task perfromance, and that these effects may vary for different architectural designs {---} fully-connected, convolutional or recurrent. Specifically, we hypothesize that morpheme-based representations will have advantages in terms of their generalization capacity and task accuracy, due to their better OOV coverage. To empirically study these effects, we develop a new sentiment analysis benchmark for Hebrew, based on 12K social media comments, and provide two instances of these data: in token-based and morpheme-based settings. Our experiments show that representation choices empirical effects vary with architecture type. While fully-connected and convolutional networks slightly prefer token-based settings, RNNs benefit from a morpheme-based representation, in accord with the hypothesis that explicit morphological information may help generalize. Our endeavour also delivers the first state-of-the-art broad-coverage sentiment analyzer for Hebrew, with over 89{\\%} accuracy, alongside an established benchmark to further study the effects of linguistic representation choices on neural networks{'} task performance.\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: hi\nLanguage family: Indo-European\nGenus: Indic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SOV\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SONegV\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_semeval_2020,\n title = \"{S}em{E}val-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets\",\n author = {Patwa, Parth and\n Aguilar, Gustavo and\n Kar, Sudipta and\n Pandey, Suraj and\n PYKL, Srinivas and\n Gamb{\\\"a}ck, Bj{\\\"o}rn and\n Chakraborty, Tanmoy and\n Solorio, Thamar and\n Das, Amitava},\n booktitle = \"Proceedings of the Fourteenth Workshop on Semantic Evaluation\",\n month = dec,\n year = \"2020\",\n address = \"Barcelona (online)\",\n publisher = \"International Committee for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.semeval-1.100\",\n doi = \"10.18653/v1/2020.semeval-1.100\",\n pages = \"774--790\",\n}\n\n\n\n\n\nDomain: news\nLanguage: hr\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 5\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: other\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@Article{dataset_hr_sentiment_news_document,\n AUTHOR = {Pelicon, Andraž and Pranjić, Marko and Miljković, Dragana and Škrlj, Blaž and Pollak, Senja},\n TITLE = {Zero-Shot Learning for Cross-Lingual News Sentiment Classification},\n JOURNAL = {Applied Sciences},\n VOLUME = {10},\n YEAR = {2020},\n NUMBER = {17},\n ARTICLE-NUMBER = {5993},\n URL = {https://www.mdpi.com/2076-3417/10/17/5993},\n ISSN = {2076-3417},\n ABSTRACT = {In this paper, we address the task of zero-shot cross-lingual news sentiment classification. Given the annotated dataset of positive, neutral, and negative news in Slovene, the aim is to develop a news classification system that assigns the sentiment category not only to Slovene news, but to news in another language without any training data required. Our system is based on the multilingual BERTmodel, while we test different approaches for handling long documents and propose a novel technique for sentiment enrichment of the BERT model as an intermediate training step. With the proposed approach, we achieve state-of-the-art performance on the sentiment analysis task on Slovenian news. We evaluate the zero-shot cross-lingual capabilities of our system on a novel news sentiment test set in Croatian. The results show that the cross-lingual approach also largely outperforms the majority classifier, as well as all settings without sentiment enrichment in pre-training.},\n DOI = {10.3390/app10175993}\n}\n\n\n\n\n\nDomain: social_media\nLanguage: hr\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 5\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: other\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: hu\nLanguage family: Uralic\nGenus: Ugric\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 10 or more\nOrder of subject, object, verb: no dominant order\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: it\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_it_evalita2016,\n TITLE = {{Overview of the Evalita 2016 SENTIment POLarity Classification Task}},\n AUTHOR = {Barbieri, Francesco and Basile, Valerio and Croce, Danilo and Nissim, Malvina and Novielli, Nicole and Patti, Viviana},\n URL = {https://hal.inria.fr/hal-01414731},\n BOOKTITLE = {{Proceedings of Third Italian Conference on Computational Linguistics (CLiC-it 2016) \\& Fifth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2016)}},\n ADDRESS = {Naples, Italy},\n YEAR = {2016},\n MONTH = Dec,\n KEYWORDS = {Natural language processing and web ; Social media analysis ; Sentiment analysis},\n PDF = {https://hal.inria.fr/hal-01414731/file/paper_026.pdf},\n HAL_ID = {hal-01414731},\n HAL_VERSION = {v1},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: it\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_it_multiemotions,\n author = {Sprugnoli, Rachele},\n year = {2020},\n month = {12},\n pages = {},\n title = {MultiEmotions-It: a New Dataset for Opinion Polarity and Emotion Analysis for Italian},\n booktitle = {Proceedings of the Seventh Italian Conference on Computational Linguistics},\n}\n\n\n\n\n\nDomain: reviews\nLanguage: ja\nLanguage family: Japanese\nGenus: Japanese\nDefinite articles: no article\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 8-9\nOrder of subject, object, verb: SOV\nNegative morphemes: negative affix\nPolar questions: question particle\nPosition of negative word wrt SOV: MorphNeg\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_multilan_amazon,\n title = \"The Multilingual {A}mazon Reviews Corpus\",\n author = {Keung, Phillip and\n Lu, Yichao and\n Szarvas, Gy{\\\"o}rgy and\n Smith, Noah A.},\n booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.emnlp-main.369\",\n doi = \"10.18653/v1/2020.emnlp-main.369\",\n pages = \"4563--4568\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: lv\nLanguage family: Indo-European\nGenus: Baltic\nDefinite articles: demonstrative word used as definite article\nIndefinite articles: indefinite word same as one\nNumber of cases: 5\nOrder of subject, object, verb: SVO\nNegative morphemes: negative affix\nPolar questions: question particle\nPosition of negative word wrt SOV: MorphNeg\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@article{dataset_lv_ltec_sentiment,\n author = {Uga Sprogis and\n Matiss Rikters},\n title = {What Can We Learn From Almost a Decade of Food Tweets},\n journal = {Computing Research Repository},\n volume = {arXiv:2007.05194},\n note = {Version 2},\n year = {2020},\n url = {https://arxiv.org/abs/2007.05194},\n eprinttype = {arXiv},\n eprint = {2007.05194},\n timestamp = {Mon, 20 Jul 2020 14:20:39 +0200},\n biburl = {https://dblp.org/rec/journals/corr/abs-2007-05194.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n\n\n\n\nDomain: reviews\nLanguage: pl\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_pl_klej_allegro_reviews,\n title = \"{KLEJ}: Comprehensive Benchmark for {P}olish Language Understanding\",\n author = \"Rybak, Piotr and\n Mroczkowski, Robert and\n Tracz, Janusz and\n Gawlik, Ireneusz\",\n booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n month = jul,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.acl-main.111\",\n doi = \"10.18653/v1/2020.acl-main.111\",\n pages = \"1191--1201\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: pl\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_pl_opi_lil_2012,\n author = {Pawel Sobkowicz and Antoni Sobkowicz},\n title ={Two-Year Study of Emotion and Communication Patterns in a Highly Polarized Political Discussion Forum},\n journal = {Social Science Computer Review},\n volume = {30},\n number = {4},\n pages = {448-469},\n year = {2012},\n doi = {10.1177/0894439312436512}\n}\n\n\n\n\n\nDomain: reviews\nLanguage: pl\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_pl_polemo,\n title = \"Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews\",\n author = \"Koco{\\'n}, Jan and\n Mi{\\l}kowski, Piotr and\n Za{\\'s}ko-Zieli{\\'n}ska, Monika\",\n booktitle = \"Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)\",\n month = nov,\n year = \"2019\",\n address = \"Hong Kong, China\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/K19-1092\",\n doi = \"10.18653/v1/K19-1092\",\n pages = \"980--991\"\n}\n\n\n\n\n\nDomain: social_media\nLanguage: pl\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: pt\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_dai_labor,\n author = {Narr, Sascha and Michael Hülfenhaus and Albayrak, Sahin},\n title = {Language-Independent Twitter Sentiment Analysis},\n booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},\n year = {2012},\n location = {Dortmund, Germany},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: pt\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_dai_labor,\n author = {Narr, Sascha and Michael Hülfenhaus and Albayrak, Sahin},\n title = {Language-Independent Twitter Sentiment Analysis},\n booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},\n year = {2012},\n location = {Dortmund, Germany},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: pt\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_pt_tweet_sent_br,\n title = \"Building a Sentiment Corpus of Tweets in {B}razilian {P}ortuguese\",\n author = \"Brum, Henrico and\n Volpe Nunes, Maria das Gra{\\c{c}}as\",\n booktitle = \"Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)\",\n month = may,\n year = \"2018\",\n address = \"Miyazaki, Japan\",\n publisher = \"European Language Resources Association (ELRA)\",\n url = \"https://aclanthology.org/L18-1658\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: pt\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: ru\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_ru_sentiment,\n title = \"{R}u{S}entiment: An Enriched Sentiment Analysis Dataset for Social Media in {R}ussian\",\n author = \"Rogers, Anna and\n Romanov, Alexey and\n Rumshisky, Anna and\n Volkova, Svitlana and\n Gronas, Mikhail and\n Gribov, Alex\",\n booktitle = \"Proceedings of the 27th International Conference on Computational Linguistics\",\n month = aug,\n year = \"2018\",\n address = \"Santa Fe, New Mexico, USA\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/C18-1064\",\n pages = \"755--763\",\n abstract = \"This paper presents RuSentiment, a new dataset for sentiment analysis of social media posts in Russian, and a new set of comprehensive annotation guidelines that are extensible to other languages. RuSentiment is currently the largest in its class for Russian, with 31,185 posts annotated with Fleiss{'} kappa of 0.58 (3 annotations per post). To diversify the dataset, 6,950 posts were pre-selected with an active learning-style strategy. We report baseline classification results, and we also release the best-performing embeddings trained on 3.2B tokens of Russian VKontakte posts.\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: ru\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: sk\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative affix\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: MorphNeg\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: news\nLanguage: sl\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@Article{Bučar2018,\n author={Bu{\\v{c}}ar, Jo{\\v{z}}e\n and {\\v{Z}}nidar{\\v{s}}i{\\v{c}}, Martin\n and Povh, Janez},\n title={Annotated news corpora and a lexicon for sentiment analysis in Slovene},\n journal={Language Resources and Evaluation},\n year={2018},\n month={Sep},\n day={01},\n volume={52},\n number={3},\n pages={895-919},\n abstract={In this study, we introduce Slovene web-crawled news corpora with sentiment annotation on three levels of granularity: sentence, paragraph and document levels. We describe the methodology and tools that were required for their construction. The corpora contain more than 250,000 documents with political, business, economic and financial content from five Slovene media resources on the web. More than 10,000 of them were manually annotated as negative, neutral or positive. All corpora are publicly available under a Creative Commons copyright license. We used the annotated documents to construct a Slovene sentiment lexicon, which is the first of its kind for Slovene, and to assess the sentiment classification approaches used. The constructed corpora were also utilised to monitor within-the-document sentiment dynamics, its changes over time and relations with news topics. We show that sentiment is, on average, more explicit at the beginning of documents, and it loses sharpness towards the end of documents.},\n issn={1574-0218},\n doi={10.1007/s10579-018-9413-3},\n url={https://doi.org/10.1007/s10579-018-9413-3}\n}\n\n\n\n\n\nDomain: social_media\nLanguage: sl\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: sq\nLanguage family: Indo-European\nGenus: Albanian\nDefinite articles: definite affix\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 4\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: reviews\nLanguage: sr\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 5\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: other\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_sr_serb_movie_reviews,\n title = \"Reliable Baselines for Sentiment Analysis in Resource-Limited Languages: The {S}erbian Movie Review Dataset\",\n author = \"Batanovi{\\'c}, Vuk and\n Nikoli{\\'c}, Bo{\\v{s}}ko and\n Milosavljevi{\\'c}, Milan\",\n booktitle = \"Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)\",\n month = may,\n year = \"2016\",\n address = \"Portoro{\\v{z}}, Slovenia\",\n publisher = \"European Language Resources Association (ELRA)\",\n url = \"https://aclanthology.org/L16-1427\",\n pages = \"2688--2696\",\n abstract = \"Collecting data for sentiment analysis in resource-limited languages carries a significant risk of sample selection bias, since the small quantities of available data are most likely not representative of the whole population. Ignoring this bias leads to less robust machine learning classifiers and less reliable evaluation results. In this paper we present a dataset balancing algorithm that minimizes the sample selection bias by eliminating irrelevant systematic differences between the sentiment classes. We prove its superiority over the random sampling method and we use it to create the Serbian movie review dataset ― SerbMR ― the first balanced and topically uniform sentiment analysis dataset in Serbian. In addition, we propose an incremental way of finding the optimal combination of simple text processing options and machine learning features for sentiment classification. Several popular classifiers are used in conjunction with this evaluation approach in order to establish strong but reliable baselines for sentiment analysis in Serbian.\",\n}\n\n\n\n\n\nDomain: reviews\nLanguage: sr\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 5\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: other\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_sr_senticomments,\n doi = {10.1371/journal.pone.0242050},\n author = {Batanović, Vuk AND Cvetanović, Miloš AND Nikolić, Boško},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {A versatile framework for resource-limited sentiment articulation, annotation, and analysis of short texts},\n year = {2020},\n month = {11},\n volume = {15},\n url = {https://doi.org/10.1371/journal.pone.0242050},\n pages = {1-30},\n abstract = {Choosing a comprehensive and cost-effective way of articulating and annotating the sentiment of a text is not a trivial task, particularly when dealing with short texts, in which sentiment can be expressed through a wide variety of linguistic and rhetorical phenomena. This problem is especially conspicuous in resource-limited settings and languages, where design options are restricted either in terms of manpower and financial means required to produce appropriate sentiment analysis resources, or in terms of available language tools, or both. In this paper, we present a versatile approach to addressing this issue, based on multiple interpretations of sentiment labels that encode information regarding the polarity, subjectivity, and ambiguity of a text, as well as the presence of sarcasm or a mixture of sentiments. We demonstrate its use on Serbian, a resource-limited language, via the creation of a main sentiment analysis dataset focused on movie comments, and two smaller datasets belonging to the movie and book domains. In addition to measuring the quality of the annotation process, we propose a novel metric to validate its cost-effectiveness. Finally, the practicality of our approach is further validated by training, evaluating, and determining the optimal configurations of several different kinds of machine-learning models on a range of sentiment classification tasks using the produced dataset.},\n number = {11},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: sr\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 5\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: other\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: sv\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite affix\nIndefinite articles: indefinite word same as one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: more than one position\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: common, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: th\nLanguage family: Tai-Kadai\nGenus: Kam-Tai\nDefinite articles: no article\nIndefinite articles: indefinite word distinct from one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative auxiliary verb\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: little affixation\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: noun classifiers\n\n@misc{dataset_th_wisesight_sentiment,\n author = {Suriyawongkul, Arthit and\n Chuangsuwanich, Ekapol and\n Chormai, Pattarawat and\n Polpanumas, Charin},\n title = {PyThaiNLP/wisesight-sentiment: First release (v1.0)},\n month = sep,\n year = 2019,\n publisher = {Zenodo},\n version = {v1.0},\n doi = {10.5281/zenodo.3457447},\n url = {https://doi.org/10.5281/zenodo.3457447},\n note = {Zenodo}\n}\n\n\n\n\n\nDomain: reviews\nLanguage: th\nLanguage family: Tai-Kadai\nGenus: Kam-Tai\nDefinite articles: no article\nIndefinite articles: indefinite word distinct from one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative auxiliary verb\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: little affixation\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: noun classifiers\n\n@misc{dataset_th_wongnai_reviews,\n author = {Ekkalak Thongthanomkul and Tanapol Nearunchorn and Yuwat Chuesathuchon},\n title = {wongnai-corpus},\n year = {2019},\n publisher = {GitHub},\n journal = {GitHub repository},\n howpublished = {\\url{https://github.com/wongnai/wongnai-corpus}}\n}\n\n\n\n\n\nDomain: mixed\nLanguage: ur\nLanguage family: Indo-European\nGenus: Indic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 2\nOrder of subject, object, verb: SOV\nNegative morphemes: negative affix\nPolar questions: question particle\nPosition of negative word wrt SOV: SONegV\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@InProceedings{dataset_ur_roman_urdu,\n title = \"Performing Natural Language Processing on Roman Urdu Datasets\",\n author = \"Zareen Sharf and Saif Ur Rahman\",\n booktitle = \"International Journal of Computer Science and Network Security\",\n volume = \"18\",\n pages = \"141-148\",\n year = \"2018\",\n url = {http://paper.ijcsns.org/07_book/201801/20180117.pdf}\n}\n\n\n\n\n\nDomain: reviews\nLanguage: zh\nLanguage family: Sino-Tibetan\nGenus: Chinese\nDefinite articles: no article\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: little affixation\nCoding of nominal plurality: no plural\nGrammatical genders: noun classifiers\n\n@inproceedings{dataset_zh_hotel_reviews,\n title = \"An Empirical Study on Sentiment Classification of {C}hinese Review using Word Embedding\",\n author = \"Lin, Yiou and\n Lei, Hang and\n Wu, Jia and\n Li, Xiaoyu\",\n booktitle = \"Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters\",\n month = oct,\n year = \"2015\",\n address = \"Shanghai, China\",\n url = \"https://aclanthology.org/Y15-2030\",\n pages = \"258--266\",\n}\n\n\n\n\nDomain: reviews\nLanguage: zh\nLanguage family: Sino-Tibetan\nGenus: Chinese\nDefinite articles: no article\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: little affixation\nCoding of nominal plurality: no plural\nGrammatical genders: noun classifiers\n\n@inproceedings{dataset_multilan_amazon,\n title = \"The Multilingual {A}mazon Reviews Corpus\",\n author = {Keung, Phillip and\n Lu, Yichao and\n Szarvas, Gy{\\\"o}rgy and\n Smith, Noah A.},\n booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.emnlp-main.369\",\n doi = \"10.18653/v1/2020.emnlp-main.369\",\n pages = \"4563--4568\",\n}" + }, + { + "objectID": "citations.html#citations", + "href": "citations.html#citations", + "title": "MMS Dataset Citations", + "section": "", + "text": "Domain: social_media\nLanguage: ar\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: masculine, feminine\n\n@InProceedings{dataset_ar_arsentdl,\n author = {Ramy Baly and\n Alaa Khaddaj and\n Hazem M. Hajj and\n Wassim El{-}Hajj and\n Khaled Bashir Shaban},\n title = {{ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets}},\n booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},\n year = {2018},\n month = {may},\n date = {7-12},\n location = {Miyazaki, Japan},\n editor = {Hend Al-Khalifa and King Saud University and KSA Walid Magdy and University of Edinburgh and UK Kareem Darwish and Qatar Computing Research Institute and Qatar Tamer Elsayed and Qatar University and Qatar},\n publisher = {European Language Resources Association (ELRA)},\n address = {Paris, France},\n isbn = {979-10-95546-25-2},\n language = {english}\n}\n\n\n\n\n\nDomain: social_media\nLanguage: ar\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_ar_astd,\n title = \"{ASTD}: {A}rabic Sentiment Tweets Dataset\",\n author = \"Nabil, Mahmoud and\n Aly, Mohamed and\n Atiya, Amir\",\n booktitle = \"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing\",\n month = sep,\n year = \"2015\",\n address = \"Lisbon, Portugal\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/D15-1299\",\n doi = \"10.18653/v1/D15-1299\",\n pages = \"2515--2519\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: ar\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_ar_bbn,\n title = \"Sentiment after Translation: A Case-Study on {A}rabic Social Media Posts\",\n author = \"Salameh, Mohammad and\n Mohammad, Saif and\n Kiritchenko, Svetlana\",\n booktitle = \"Proceedings of the 2015 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies\",\n month = may # \"{--}\" # jun,\n year = \"2015\",\n address = \"Denver, Colorado\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/N15-1078\",\n doi = \"10.3115/v1/N15-1078\",\n pages = \"767--777\",\n}\n\n\n\n\n\nDomain: reviews\nLanguage: ar\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: masculine, feminine\n\n@INPROCEEDINGS{dataset_ar_brad,\n author={Elnagar, Ashraf and Einea, Omar},\n booktitle={2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)}, \n title={{BRAD} 1.0: Book reviews in Arabic dataset}, \n year={2016},\n volume={},\n number={},\n pages={1-8},\n doi={10.1109/AICCSA.2016.7945800}\n}\n\n\n\n\n\nDomain: reviews\nLanguage: ar\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: masculine, feminine\n\n@Book{dataset_ar_hard,\n author=\"Elnagar, Ashraf\n and Khalifa, Yasmin S.\n and Einea, Anas\",\n title={Hotel Arabic-Reviews Dataset Construction for Sentiment Analysis Applications},\n bookTitle=\"Intelligent Natural Language Processing: Trends and Applications\",\n year=\"2018\",\n publisher=\"Springer International Publishing\",\n address=\"Cham\",\n pages=\"35--52\",\n isbn=\"978-3-319-67056-0\",\n doi=\"10.1007/978-3-319-67056-0_3\",\n url=\"https://doi.org/10.1007/978-3-319-67056-0_3\"\n}\n\n\n\n\n\nDomain: reviews\nLanguage: ar\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_ar_labr,\n title = \"{LABR}: A Large Scale {A}rabic Book Reviews Dataset\",\n author = \"Aly, Mohamed and\n Atiya, Amir\",\n booktitle = \"Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)\",\n month = aug,\n year = \"2013\",\n address = \"Sofia, Bulgaria\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/P13-2088\",\n pages = \"494--498\",\n}\n\n\n\n\n\nDomain: reviews\nLanguage: ar\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_ar_oclar,\n author={Al Omari, Marwan and Al-Hajj, Moustafa and Hammami, Nacereddine and Sabra, Amani},\n booktitle={2019 International Conference on Computer and Information Sciences (ICCIS)}, \n title={Sentiment Classifier: Logistic Regression for Arabic Services’ Reviews in Lebanon}, \n year={2019},\n volume={},\n number={},\n pages={1-5},\n doi={10.1109/ICCISci.2019.8716394}\n}\n\n\n\n\n\nDomain: mixed\nLanguage: ar\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_semeval_2017,\n title = \"{S}em{E}val-2017 Task 4: Sentiment Analysis in {T}witter\",\n author = \"Rosenthal, Sara and\n Farra, Noura and\n Nakov, Preslav\",\n booktitle = \"Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)\",\n month = aug,\n year = \"2017\",\n address = \"Vancouver, Canada\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/S17-2088\",\n doi = \"10.18653/v1/S17-2088\",\n pages = \"502--518\",\n abstract = \"This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: ar\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_ar_bbn,\n title = \"Sentiment after Translation: A Case-Study on {A}rabic Social Media Posts\",\n author = \"Salameh, Mohammad and\n Mohammad, Saif and\n Kiritchenko, Svetlana\",\n booktitle = \"Proceedings of the 2015 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies\",\n month = may # \"{--}\" # jun,\n year = \"2015\",\n address = \"Denver, Colorado\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/N15-1078\",\n doi = \"10.3115/v1/N15-1078\",\n pages = \"767--777\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: bg\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: no article\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: bs\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 5\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: other\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: cs\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative affix\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: MorphNeg\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_cs_social_media,\n title = \"Sentiment Analysis in {C}zech Social Media Using Supervised Machine Learning\",\n author = \"Habernal, Ivan and\n Pt{\\'a}{\\v{c}}ek, Tom{\\'a}{\\v{s}} and\n Steinberger, Josef\",\n booktitle = \"Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis\",\n month = jun,\n year = \"2013\",\n address = \"Atlanta, Georgia\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/W13-1609\",\n pages = \"65--74\",\n}\n\n\n\n\n\nDomain: reviews\nLanguage: cs\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative affix\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: MorphNeg\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_cs_social_media,\n title = \"Sentiment Analysis in {C}zech Social Media Using Supervised Machine Learning\",\n author = \"Habernal, Ivan and\n Pt{\\'a}{\\v{c}}ek, Tom{\\'a}{\\v{s}} and\n Steinberger, Josef\",\n booktitle = \"Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis\",\n month = jun,\n year = \"2013\",\n address = \"Atlanta, Georgia\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/W13-1609\",\n pages = \"65--74\",\n}\n\n\n\n\n\nDomain: reviews\nLanguage: cs\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative affix\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: MorphNeg\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_cs_social_media,\n title = \"Sentiment Analysis in {C}zech Social Media Using Supervised Machine Learning\",\n author = \"Habernal, Ivan and\n Pt{\\'a}{\\v{c}}ek, Tom{\\'a}{\\v{s}} and\n Steinberger, Josef\",\n booktitle = \"Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis\",\n month = jun,\n year = \"2013\",\n address = \"Atlanta, Georgia\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/W13-1609\",\n pages = \"65--74\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: cs\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative affix\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: MorphNeg\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_cs_social_media,\n title = \"Sentiment Analysis in {C}zech Social Media Using Supervised Machine Learning\",\n author = \"Habernal, Ivan and\n Pt{\\'a}{\\v{c}}ek, Tom{\\'a}{\\v{s}} and\n Steinberger, Josef\",\n booktitle = \"Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis\",\n month = jun,\n year = \"2013\",\n address = \"Atlanta, Georgia\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/W13-1609\",\n pages = \"65--74\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: de\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: 4\nOrder of subject, object, verb: no dominant order\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: more than one position\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_dai_labor,\n author = {Narr, Sascha and Michael Hülfenhaus and Albayrak, Sahin},\n title = {Language-Independent Twitter Sentiment Analysis},\n booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},\n year = {2012},\n location = {Dortmund, Germany},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: de\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: 4\nOrder of subject, object, verb: no dominant order\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: more than one position\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_dai_labor,\n author = {Narr, Sascha and Michael Hülfenhaus and Albayrak, Sahin},\n title = {Language-Independent Twitter Sentiment Analysis},\n booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},\n year = {2012},\n location = {Dortmund, Germany},\n}\n\n\n\n\n\nDomain: reviews\nLanguage: de\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: 4\nOrder of subject, object, verb: no dominant order\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: more than one position\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_multilan_amazon,\n title = \"The Multilingual {A}mazon Reviews Corpus\",\n author = {Keung, Phillip and\n Lu, Yichao and\n Szarvas, Gy{\\\"o}rgy and\n Smith, Noah A.},\n booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.emnlp-main.369\",\n doi = \"10.18653/v1/2020.emnlp-main.369\",\n pages = \"4563--4568\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: de\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: 4\nOrder of subject, object, verb: no dominant order\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: more than one position\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_de_omp,\n title = \"Academic-Industrial Perspective on the Development and Deployment of a Moderation System for a Newspaper Website\",\n author = \"Schabus, Dietmar and\n Skowron, Marcin\",\n booktitle = \"Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)\",\n month = may,\n year = \"2018\",\n address = \"Miyazaki, Japan\",\n publisher = \"European Language Resources Association (ELRA)\",\n url = \"https://aclanthology.org/L18-1253\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: de\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: 4\nOrder of subject, object, verb: no dominant order\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: more than one position\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_de_sb10k,\n title = \"A {T}witter Corpus and Benchmark Resources for {G}erman Sentiment Analysis\",\n author = \"Cieliebak, Mark and\n Deriu, Jan Milan and\n Egger, Dominic and\n Uzdilli, Fatih\",\n booktitle = \"Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media\",\n month = apr,\n year = \"2017\",\n address = \"Valencia, Spain\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/W17-1106\",\n doi = \"10.18653/v1/W17-1106\",\n pages = \"45--51\",\n abstract = \"In this paper we present SB10k, a new corpus for sentiment analysis with approx. 10,000 German tweets. We use this new corpus and two existing corpora to provide state-of-the-art benchmarks for sentiment analysis in German: we implemented a CNN (based on the winning system of SemEval-2016) and a feature-based SVM and compare their performance on all three corpora. For the CNN, we also created German word embeddings trained on 300M tweets. These word embeddings were then optimized for sentiment analysis using distant-supervised learning. The new corpus, the German word embeddings (plain and optimized), and source code to re-run the benchmarks are publicly available.\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: de\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: 4\nOrder of subject, object, verb: no dominant order\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: more than one position\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: reviews\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_en_amazon,\n title = \"Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects\",\n author = \"Ni, Jianmo and\n Li, Jiacheng and\n McAuley, Julian\",\n booktitle = \"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)\",\n month = nov,\n year = \"2019\",\n address = \"Hong Kong, China\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/D19-1018\",\n doi = \"10.18653/v1/D19-1018\",\n pages = \"188--197\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_dai_labor,\n author = {Narr, Sascha and Michael Hülfenhaus and Albayrak, Sahin},\n title = {Language-Independent Twitter Sentiment Analysis},\n booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},\n year = {2012},\n location = {Dortmund, Germany},\n}\n\n\n\n\n\nDomain: news\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@article{dataset_en_financial_phrasebank_sentences_75agree,\n author = {Malo, Pekka and Sinha, Ankur and Korhonen, Pekka and Wallenius, Jyrki and Takala, Pyry},\n title = {Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts},\n year = {2014},\n issue_date = {April 2014},\n publisher = {John Wiley & Sons, Inc.},\n address = {USA},\n volume = {65},\n number = {4},\n issn = {2330-1635},\n url = {https://doi.org/10.1002/asi.23062},\n doi = {10.1002/asi.23062},\n journal = {Journal of the Association for Information Science and Technology},\n month = {apr},\n pages = {782–796},\n numpages = {15},\n keywords = {economics, automatic classification, linguistic analysis}\n}\n\n\n\n\n\nDomain: reviews\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_multilan_amazon,\n title = \"The Multilingual {A}mazon Reviews Corpus\",\n author = {Keung, Phillip and\n Lu, Yichao and\n Szarvas, Gy{\\\"o}rgy and\n Smith, Noah A.},\n booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.emnlp-main.369\",\n doi = \"10.18653/v1/2020.emnlp-main.369\",\n pages = \"4563--4568\",\n}\n\n\n\n\n\nDomain: news\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_en_per_sent,\n title = \"Author{'}s Sentiment Prediction\",\n author = \"Bastan, Mohaddeseh and\n Koupaee, Mahnaz and\n Son, Youngseo and\n Sicoli, Richard and\n Balasubramanian, Niranjan\",\n booktitle = \"Proceedings of the 28th International Conference on Computational Linguistics\",\n month = dec,\n year = \"2020\",\n address = \"Barcelona, Spain (Online)\",\n publisher = \"International Committee on Computational Linguistics\",\n url = \"https://aclanthology.org/2020.coling-main.52\",\n doi = \"10.18653/v1/2020.coling-main.52\",\n pages = \"604--615\",\n}\n\n\n\n\n\nDomain: poems\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_en_poem_sentiment,\n title = \"Investigating Societal Biases in a Poetry Composition System\",\n author = \"Sheng, Emily and\n Uthus, David\",\n booktitle = \"Proceedings of the Second Workshop on Gender Bias in Natural Language Processing\",\n month = dec,\n year = \"2020\",\n address = \"Barcelona, Spain (Online)\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.gebnlp-1.9\",\n pages = \"93--106\",\n}\n\n\n\n\n\nDomain: mixed\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_semeval_2017,\n title = \"{S}em{E}val-2017 Task 4: Sentiment Analysis in {T}witter\",\n author = \"Rosenthal, Sara and\n Farra, Noura and\n Nakov, Preslav\",\n booktitle = \"Proceedings of the 11th International Workshop on Semantic Evaluation ({S}em{E}val-2017)\",\n month = aug,\n year = \"2017\",\n address = \"Vancouver, Canada\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/S17-2088\",\n doi = \"10.18653/v1/S17-2088\",\n pages = \"502--518\",\n abstract = \"This paper describes the fifth year of the Sentiment Analysis in Twitter task. SemEval-2017 Task 4 continues with a rerun of the subtasks of SemEval-2016 Task 4, which include identifying the overall sentiment of the tweet, sentiment towards a topic with classification on a two-point and on a five-point ordinal scale, and quantification of the distribution of sentiment towards a topic across a number of tweets: again on a two-point and on a five-point ordinal scale. Compared to 2016, we made two changes: (i) we introduced a new language, Arabic, for all subtasks, and (ii) we made available information from the profiles of the Twitter users who posted the target tweets. The task continues to be very popular, with a total of 48 teams participating this year.\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@article{dataset_en_sentistrength,\n author = {Thelwall, Mike and Buckley, Kevan and Paltoglou, Georgios},\n title = {Sentiment Strength Detection for the Social Web},\n year = {2012},\n issue_date = {January 2012},\n publisher = {John Wiley \\& Sons, Inc.},\n address = {USA},\n volume = {63},\n number = {1},\n issn = {1532-2882},\n url = {https://doi.org/10.1002/asi.21662},\n doi = {10.1002/asi.21662},\n abstract = {Sentiment analysis is concerned with the automatic extraction of sentiment-related\n information from text. Although most sentiment analysis addresses commercial tass,\n such as extracting opinions from product reviews, there is increasing interest in\n the affective dimension of the social web, and Twitter in particular. Most sentiment\n analysis algorithms are not ideally suited to this task because they exploit indirect\n indicators of sentiment that can reflect genre or topic instead. Hence, such algorithms\n used to process social web texts can identify spurious sentiment patterns caused by\n topics rather than affective phenomena. This article assesses an improved version\n of the algorithm SentiStrength for sentiment strength detection across the social\n web that primarily uses direct indications of sentiment. The results from six diverse\n social web data sets (MySpace, Twitter, YouTube, Digg, RunnersWorld, BBCForums) indicate\n that SentiStrength 2 is successful in the sense of performing better than a baseline\n approach for all data sets in both supervised and unsupervised cases. SentiStrength\n is not always better than machine-learning approaches that exploit indirect indicators\n of sentiment, however, and is particularly weaker for positive sentiment in news-related\n discussions. Overall, the results suggest that, even unsupervised, SentiStrength is\n robust enough to be applied to a wide variety of different social web contexts.},\n journal = {J. Am. Soc. Inf. Sci. Technol.},\n month = jan,\n pages = {163–173},\n numpages = {11}\n}\n\n\n\n\n\nDomain: chats\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_en_silicone,\n title = \"Hierarchical Pre-training for Sequence Labelling in Spoken Dialog\",\n author = \"Chapuis, Emile and\n Colombo, Pierre and\n Manica, Matteo and\n Labeau, Matthieu and\n Clavel, Chlo{\\'e}\",\n booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2020\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.findings-emnlp.239\",\n doi = \"10.18653/v1/2020.findings-emnlp.239\",\n pages = \"2636--2648\",\n}\n\n\n\n\n\nDomain: chats\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_en_silicone,\n title = \"Hierarchical Pre-training for Sequence Labelling in Spoken Dialog\",\n author = \"Chapuis, Emile and\n Colombo, Pierre and\n Manica, Matteo and\n Labeau, Matthieu and\n Clavel, Chlo{\\'e}\",\n booktitle = \"Findings of the Association for Computational Linguistics: EMNLP 2020\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.findings-emnlp.239\",\n doi = \"10.18653/v1/2020.findings-emnlp.239\",\n pages = \"2636--2648\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@misc{dataset_en_tweet_airlines,\n url={https://www.kaggle.com/crowdflower/twitter-airline-sentiment},\n author={Crowdflower Inc.},\n title={Twitter US Airline Sentiment},\n year={2015}\n}\n\n\n\n\n\nDomain: social_media\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@article{dataset_en_tweets_sanders,\n title={{Sanders-Twitter Sentiment Corpus}},\n author={Sanders, Niek J},\n journal={Sanders Analytics LLC},\n year={2011}\n}\n\n\n\n\n\nDomain: social_media\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: reviews\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_en_vader,\n title={{VADER}: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text},\n author={Clayton J. Hutto and Eric Gilbert},\n booktitle={Proceedings of the International AAAI Conference on Web and Social Media},\n year={2014},\n url={https://ojs.aaai.org/index.php/ICWSM/article/view/14550},\n month={May}, \n pages={216-225},\n volume=8,\n}\n\n\n\n\n\nDomain: reviews\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_en_vader,\n title={{VADER}: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text},\n author={Clayton J. Hutto and Eric Gilbert},\n booktitle={Proceedings of the International AAAI Conference on Web and Social Media},\n year={2014},\n url={https://ojs.aaai.org/index.php/ICWSM/article/view/14550},\n month={May}, \n pages={216-225},\n volume=8,\n}\n\n\n\n\n\nDomain: news\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_en_vader,\n title={{VADER}: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text},\n author={Clayton J. Hutto and Eric Gilbert},\n booktitle={Proceedings of the International AAAI Conference on Web and Social Media},\n year={2014},\n url={https://ojs.aaai.org/index.php/ICWSM/article/view/14550},\n month={May}, \n pages={216-225},\n volume=8,\n}\n\n\n\n\n\nDomain: social_media\nLanguage: en\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_en_vader,\n title={{VADER}: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text},\n author={Clayton J. Hutto and Eric Gilbert},\n booktitle={Proceedings of the International AAAI Conference on Web and Social Media},\n year={2014},\n url={https://ojs.aaai.org/index.php/ICWSM/article/view/14550},\n month={May}, \n pages={216-225},\n volume=8,\n}\n\n\n\n\n\nDomain: reviews\nLanguage: es\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@article{dataset_es_muchocine,\n title={Experiments in sentiment classification of movie reviews in Spanish},\n author={Cruz, Fermin L and Troyano, Jose A and Enriquez, Fernando and Ortega, Javier},\n journal={Procesamiento del Lenguaje Natural},\n volume={41},\n pages={73--80},\n year={2008},\n publisher={SOC ESPANOLA PROCESAMIENTO LENGUAJE NATURAL-SEPLN DEPT LENGUAJES \\& SISTEMAS~…}\n}\n\n\n\n\n\nDomain: reviews\nLanguage: es\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_multilan_amazon,\n title = \"The Multilingual {A}mazon Reviews Corpus\",\n author = {Keung, Phillip and\n Lu, Yichao and\n Szarvas, Gy{\\\"o}rgy and\n Smith, Noah A.},\n booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.emnlp-main.369\",\n doi = \"10.18653/v1/2020.emnlp-main.369\",\n pages = \"4563--4568\",\n}\n\n\n\n\n\nDomain: reviews\nLanguage: es\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@article{dataset_es_paper_reviews,\n author = {Keith Norambuena, Brian and Lettura, Exequiel and Villegas, Claudio},\n year = {2019},\n month = {02},\n pages = {191-214},\n title = {Sentiment analysis and opinion mining applied to scientific paper reviews},\n volume = {23},\n journal = {Intelligent Data Analysis},\n doi = {10.3233/IDA-173807}\n}\n\n\n\n\n\nDomain: social_media\nLanguage: es\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_semeval_2020,\n title = \"{S}em{E}val-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets\",\n author = {Patwa, Parth and\n Aguilar, Gustavo and\n Kar, Sudipta and\n Pandey, Suraj and\n PYKL, Srinivas and\n Gamb{\\\"a}ck, Bj{\\\"o}rn and\n Chakraborty, Tanmoy and\n Solorio, Thamar and\n Das, Amitava},\n booktitle = \"Proceedings of the Fourteenth Workshop on Semantic Evaluation\",\n month = dec,\n year = \"2020\",\n address = \"Barcelona (online)\",\n publisher = \"International Committee for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.semeval-1.100\",\n doi = \"10.18653/v1/2020.semeval-1.100\",\n pages = \"774--790\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: es\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: reviews\nLanguage: fa\nLanguage family: Indo-European\nGenus: Iranian\nDefinite articles: no article\nIndefinite articles: indefinite word same as one\nNumber of cases: 2\nOrder of subject, object, verb: SOV\nNegative morphemes: negative affix\nPolar questions: question particle\nPosition of negative word wrt SOV: MorphNeg\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@article{dataset_fa_sentipers,\n author = {Pedram Hosseini and\n Ali Ahmadian Ramaki and\n Hassan Maleki and\n Mansoureh Anvari and\n Seyed Abolghasem Mirroshandel},\n title = {{SentiPers}: {A} Sentiment Analysis Corpus for Persian},\n journal = {Computing Research Repository},\n volume = {arXiv:1801.07737},\n note = {Version 2},\n year = {2018},\n url = {http://arxiv.org/abs/1801.07737},\n eprinttype = {arXiv},\n eprint = {1801.07737},\n timestamp = {Mon, 13 Aug 2018 16:47:47 +0200},\n biburl = {https://dblp.org/rec/journals/corr/abs-1801-07737.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n\n\n\n\nDomain: social_media\nLanguage: fr\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: OptDoubleNeg\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_dai_labor,\n author = {Narr, Sascha and Michael Hülfenhaus and Albayrak, Sahin},\n title = {Language-Independent Twitter Sentiment Analysis},\n booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},\n year = {2012},\n location = {Dortmund, Germany},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: fr\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: OptDoubleNeg\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_dai_labor,\n author = {Narr, Sascha and Michael Hülfenhaus and Albayrak, Sahin},\n title = {Language-Independent Twitter Sentiment Analysis},\n booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},\n year = {2012},\n location = {Dortmund, Germany},\n}\n\n\n\n\n\nDomain: reviews\nLanguage: fr\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: OptDoubleNeg\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_multilan_amazon,\n title = \"The Multilingual {A}mazon Reviews Corpus\",\n author = {Keung, Phillip and\n Lu, Yichao and\n Szarvas, Gy{\\\"o}rgy and\n Smith, Noah A.},\n booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.emnlp-main.369\",\n doi = \"10.18653/v1/2020.emnlp-main.369\",\n pages = \"4563--4568\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: he\nLanguage family: Afro-Asiatic\nGenus: Semitic\nDefinite articles: definite affix\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_he_hebrew_sentiment,\n title = \"Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from {M}odern {H}ebrew\",\n author = \"Amram, Adam and\n Ben David, Anat and\n Tsarfaty, Reut\",\n booktitle = \"Proceedings of the 27th International Conference on Computational Linguistics\",\n month = aug,\n year = \"2018\",\n address = \"Santa Fe, New Mexico, USA\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/C18-1190\",\n pages = \"2242--2252\",\n abstract = \"This paper empirically studies the effects of representation choices on neural sentiment analysis for Modern Hebrew, a morphologically rich language (MRL) for which no sentiment analyzer currently exists. We study two dimensions of representational choices: (i) the granularity of the input signal (token-based vs. morpheme-based), and (ii) the level of encoding of vocabulary items (string-based vs. character-based). We hypothesise that for MRLs, languages where multiple meaning-bearing elements may be carried by a single space-delimited token, these choices will have measurable effects on task perfromance, and that these effects may vary for different architectural designs {---} fully-connected, convolutional or recurrent. Specifically, we hypothesize that morpheme-based representations will have advantages in terms of their generalization capacity and task accuracy, due to their better OOV coverage. To empirically study these effects, we develop a new sentiment analysis benchmark for Hebrew, based on 12K social media comments, and provide two instances of these data: in token-based and morpheme-based settings. Our experiments show that representation choices empirical effects vary with architecture type. While fully-connected and convolutional networks slightly prefer token-based settings, RNNs benefit from a morpheme-based representation, in accord with the hypothesis that explicit morphological information may help generalize. Our endeavour also delivers the first state-of-the-art broad-coverage sentiment analyzer for Hebrew, with over 89{\\%} accuracy, alongside an established benchmark to further study the effects of linguistic representation choices on neural networks{'} task performance.\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: hi\nLanguage family: Indo-European\nGenus: Indic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 3\nOrder of subject, object, verb: SOV\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SONegV\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_semeval_2020,\n title = \"{S}em{E}val-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets\",\n author = {Patwa, Parth and\n Aguilar, Gustavo and\n Kar, Sudipta and\n Pandey, Suraj and\n PYKL, Srinivas and\n Gamb{\\\"a}ck, Bj{\\\"o}rn and\n Chakraborty, Tanmoy and\n Solorio, Thamar and\n Das, Amitava},\n booktitle = \"Proceedings of the Fourteenth Workshop on Semantic Evaluation\",\n month = dec,\n year = \"2020\",\n address = \"Barcelona (online)\",\n publisher = \"International Committee for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.semeval-1.100\",\n doi = \"10.18653/v1/2020.semeval-1.100\",\n pages = \"774--790\",\n}\n\n\n\n\n\nDomain: news\nLanguage: hr\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 5\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: other\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@Article{dataset_hr_sentiment_news_document,\n AUTHOR = {Pelicon, Andraž and Pranjić, Marko and Miljković, Dragana and Škrlj, Blaž and Pollak, Senja},\n TITLE = {Zero-Shot Learning for Cross-Lingual News Sentiment Classification},\n JOURNAL = {Applied Sciences},\n VOLUME = {10},\n YEAR = {2020},\n NUMBER = {17},\n ARTICLE-NUMBER = {5993},\n URL = {https://www.mdpi.com/2076-3417/10/17/5993},\n ISSN = {2076-3417},\n ABSTRACT = {In this paper, we address the task of zero-shot cross-lingual news sentiment classification. Given the annotated dataset of positive, neutral, and negative news in Slovene, the aim is to develop a news classification system that assigns the sentiment category not only to Slovene news, but to news in another language without any training data required. Our system is based on the multilingual BERTmodel, while we test different approaches for handling long documents and propose a novel technique for sentiment enrichment of the BERT model as an intermediate training step. With the proposed approach, we achieve state-of-the-art performance on the sentiment analysis task on Slovenian news. We evaluate the zero-shot cross-lingual capabilities of our system on a novel news sentiment test set in Croatian. The results show that the cross-lingual approach also largely outperforms the majority classifier, as well as all settings without sentiment enrichment in pre-training.},\n DOI = {10.3390/app10175993}\n}\n\n\n\n\n\nDomain: social_media\nLanguage: hr\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 5\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: other\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: hu\nLanguage family: Uralic\nGenus: Ugric\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 10 or more\nOrder of subject, object, verb: no dominant order\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: it\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_it_evalita2016,\n TITLE = {{Overview of the Evalita 2016 SENTIment POLarity Classification Task}},\n AUTHOR = {Barbieri, Francesco and Basile, Valerio and Croce, Danilo and Nissim, Malvina and Novielli, Nicole and Patti, Viviana},\n URL = {https://hal.inria.fr/hal-01414731},\n BOOKTITLE = {{Proceedings of Third Italian Conference on Computational Linguistics (CLiC-it 2016) \\& Fifth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2016)}},\n ADDRESS = {Naples, Italy},\n YEAR = {2016},\n MONTH = Dec,\n KEYWORDS = {Natural language processing and web ; Social media analysis ; Sentiment analysis},\n PDF = {https://hal.inria.fr/hal-01414731/file/paper_026.pdf},\n HAL_ID = {hal-01414731},\n HAL_VERSION = {v1},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: it\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative intonation only\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_it_multiemotions,\n author = {Sprugnoli, Rachele},\n year = {2020},\n month = {12},\n pages = {},\n title = {MultiEmotions-It: a New Dataset for Opinion Polarity and Emotion Analysis for Italian},\n booktitle = {Proceedings of the Seventh Italian Conference on Computational Linguistics},\n}\n\n\n\n\n\nDomain: reviews\nLanguage: ja\nLanguage family: Japanese\nGenus: Japanese\nDefinite articles: no article\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 8-9\nOrder of subject, object, verb: SOV\nNegative morphemes: negative affix\nPolar questions: question particle\nPosition of negative word wrt SOV: MorphNeg\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: no grammatical gender\n\n@inproceedings{dataset_multilan_amazon,\n title = \"The Multilingual {A}mazon Reviews Corpus\",\n author = {Keung, Phillip and\n Lu, Yichao and\n Szarvas, Gy{\\\"o}rgy and\n Smith, Noah A.},\n booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.emnlp-main.369\",\n doi = \"10.18653/v1/2020.emnlp-main.369\",\n pages = \"4563--4568\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: lv\nLanguage family: Indo-European\nGenus: Baltic\nDefinite articles: demonstrative word used as definite article\nIndefinite articles: indefinite word same as one\nNumber of cases: 5\nOrder of subject, object, verb: SVO\nNegative morphemes: negative affix\nPolar questions: question particle\nPosition of negative word wrt SOV: MorphNeg\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@article{dataset_lv_ltec_sentiment,\n author = {Uga Sprogis and\n Matiss Rikters},\n title = {What Can We Learn From Almost a Decade of Food Tweets},\n journal = {Computing Research Repository},\n volume = {arXiv:2007.05194},\n note = {Version 2},\n year = {2020},\n url = {https://arxiv.org/abs/2007.05194},\n eprinttype = {arXiv},\n eprint = {2007.05194},\n timestamp = {Mon, 20 Jul 2020 14:20:39 +0200},\n biburl = {https://dblp.org/rec/journals/corr/abs-2007-05194.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n\n\n\n\n\nDomain: reviews\nLanguage: pl\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_pl_klej_allegro_reviews,\n title = \"{KLEJ}: Comprehensive Benchmark for {P}olish Language Understanding\",\n author = \"Rybak, Piotr and\n Mroczkowski, Robert and\n Tracz, Janusz and\n Gawlik, Ireneusz\",\n booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n month = jul,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.acl-main.111\",\n doi = \"10.18653/v1/2020.acl-main.111\",\n pages = \"1191--1201\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: pl\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_pl_opi_lil_2012,\n author = {Pawel Sobkowicz and Antoni Sobkowicz},\n title ={Two-Year Study of Emotion and Communication Patterns in a Highly Polarized Political Discussion Forum},\n journal = {Social Science Computer Review},\n volume = {30},\n number = {4},\n pages = {448-469},\n year = {2012},\n doi = {10.1177/0894439312436512}\n}\n\n\n\n\n\nDomain: reviews\nLanguage: pl\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_pl_polemo,\n title = \"Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews\",\n author = \"Koco{\\'n}, Jan and\n Mi{\\l}kowski, Piotr and\n Za{\\'s}ko-Zieli{\\'n}ska, Monika\",\n booktitle = \"Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)\",\n month = nov,\n year = \"2019\",\n address = \"Hong Kong, China\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/K19-1092\",\n doi = \"10.18653/v1/K19-1092\",\n pages = \"980--991\"\n}\n\n\n\n\n\nDomain: social_media\nLanguage: pl\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: pt\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_dai_labor,\n author = {Narr, Sascha and Michael Hülfenhaus and Albayrak, Sahin},\n title = {Language-Independent Twitter Sentiment Analysis},\n booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},\n year = {2012},\n location = {Dortmund, Germany},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: pt\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_dai_labor,\n author = {Narr, Sascha and Michael Hülfenhaus and Albayrak, Sahin},\n title = {Language-Independent Twitter Sentiment Analysis},\n booktitle = {Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012)},\n year = {2012},\n location = {Dortmund, Germany},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: pt\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@inproceedings{dataset_pt_tweet_sent_br,\n title = \"Building a Sentiment Corpus of Tweets in {B}razilian {P}ortuguese\",\n author = \"Brum, Henrico and\n Volpe Nunes, Maria das Gra{\\c{c}}as\",\n booktitle = \"Proceedings of the Eleventh International Conference on Language Resources and Evaluation ({LREC} 2018)\",\n month = may,\n year = \"2018\",\n address = \"Miyazaki, Japan\",\n publisher = \"European Language Resources Association (ELRA)\",\n url = \"https://aclanthology.org/L18-1658\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: pt\nLanguage family: Indo-European\nGenus: Romance\nDefinite articles: definite word distinct from demonstrative\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: ru\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_ru_sentiment,\n title = \"{R}u{S}entiment: An Enriched Sentiment Analysis Dataset for Social Media in {R}ussian\",\n author = \"Rogers, Anna and\n Romanov, Alexey and\n Rumshisky, Anna and\n Volkova, Svitlana and\n Gronas, Mikhail and\n Gribov, Alex\",\n booktitle = \"Proceedings of the 27th International Conference on Computational Linguistics\",\n month = aug,\n year = \"2018\",\n address = \"Santa Fe, New Mexico, USA\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/C18-1064\",\n pages = \"755--763\",\n abstract = \"This paper presents RuSentiment, a new dataset for sentiment analysis of social media posts in Russian, and a new set of comprehensive annotation guidelines that are extensible to other languages. RuSentiment is currently the largest in its class for Russian, with 31,185 posts annotated with Fleiss{'} kappa of 0.58 (3 annotations per post). To diversify the dataset, 6,950 posts were pre-selected with an active learning-style strategy. We report baseline classification results, and we also release the best-performing embeddings trained on 3.2B tokens of Russian VKontakte posts.\",\n}\n\n\n\n\n\nDomain: social_media\nLanguage: ru\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: sk\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative affix\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: MorphNeg\nPrefixing vs suffixing: weakly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: news\nLanguage: sl\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@Article{Bučar2018,\n author={Bu{\\v{c}}ar, Jo{\\v{z}}e\n and {\\v{Z}}nidar{\\v{s}}i{\\v{c}}, Martin\n and Povh, Janez},\n title={Annotated news corpora and a lexicon for sentiment analysis in Slovene},\n journal={Language Resources and Evaluation},\n year={2018},\n month={Sep},\n day={01},\n volume={52},\n number={3},\n pages={895-919},\n abstract={In this study, we introduce Slovene web-crawled news corpora with sentiment annotation on three levels of granularity: sentence, paragraph and document levels. We describe the methodology and tools that were required for their construction. The corpora contain more than 250,000 documents with political, business, economic and financial content from five Slovene media resources on the web. More than 10,000 of them were manually annotated as negative, neutral or positive. All corpora are publicly available under a Creative Commons copyright license. We used the annotated documents to construct a Slovene sentiment lexicon, which is the first of its kind for Slovene, and to assess the sentiment classification approaches used. The constructed corpora were also utilised to monitor within-the-document sentiment dynamics, its changes over time and relations with news topics. We show that sentiment is, on average, more explicit at the beginning of documents, and it loses sharpness towards the end of documents.},\n issn={1574-0218},\n doi={10.1007/s10579-018-9413-3},\n url={https://doi.org/10.1007/s10579-018-9413-3}\n}\n\n\n\n\n\nDomain: social_media\nLanguage: sl\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 6-7\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: sq\nLanguage family: Indo-European\nGenus: Albanian\nDefinite articles: definite affix\nIndefinite articles: indefinite word distinct from one\nNumber of cases: 4\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: reviews\nLanguage: sr\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 5\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: other\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@inproceedings{dataset_sr_serb_movie_reviews,\n title = \"Reliable Baselines for Sentiment Analysis in Resource-Limited Languages: The {S}erbian Movie Review Dataset\",\n author = \"Batanovi{\\'c}, Vuk and\n Nikoli{\\'c}, Bo{\\v{s}}ko and\n Milosavljevi{\\'c}, Milan\",\n booktitle = \"Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)\",\n month = may,\n year = \"2016\",\n address = \"Portoro{\\v{z}}, Slovenia\",\n publisher = \"European Language Resources Association (ELRA)\",\n url = \"https://aclanthology.org/L16-1427\",\n pages = \"2688--2696\",\n abstract = \"Collecting data for sentiment analysis in resource-limited languages carries a significant risk of sample selection bias, since the small quantities of available data are most likely not representative of the whole population. Ignoring this bias leads to less robust machine learning classifiers and less reliable evaluation results. In this paper we present a dataset balancing algorithm that minimizes the sample selection bias by eliminating irrelevant systematic differences between the sentiment classes. We prove its superiority over the random sampling method and we use it to create the Serbian movie review dataset ― SerbMR ― the first balanced and topically uniform sentiment analysis dataset in Serbian. In addition, we propose an incremental way of finding the optimal combination of simple text processing options and machine learning features for sentiment classification. Several popular classifiers are used in conjunction with this evaluation approach in order to establish strong but reliable baselines for sentiment analysis in Serbian.\",\n}\n\n\n\n\n\nDomain: reviews\nLanguage: sr\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 5\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: other\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_sr_senticomments,\n doi = {10.1371/journal.pone.0242050},\n author = {Batanović, Vuk AND Cvetanović, Miloš AND Nikolić, Boško},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {A versatile framework for resource-limited sentiment articulation, annotation, and analysis of short texts},\n year = {2020},\n month = {11},\n volume = {15},\n url = {https://doi.org/10.1371/journal.pone.0242050},\n pages = {1-30},\n abstract = {Choosing a comprehensive and cost-effective way of articulating and annotating the sentiment of a text is not a trivial task, particularly when dealing with short texts, in which sentiment can be expressed through a wide variety of linguistic and rhetorical phenomena. This problem is especially conspicuous in resource-limited settings and languages, where design options are restricted either in terms of manpower and financial means required to produce appropriate sentiment analysis resources, or in terms of available language tools, or both. In this paper, we present a versatile approach to addressing this issue, based on multiple interpretations of sentiment labels that encode information regarding the polarity, subjectivity, and ambiguity of a text, as well as the presence of sarcasm or a mixture of sentiments. We demonstrate its use on Serbian, a resource-limited language, via the creation of a main sentiment analysis dataset focused on movie comments, and two smaller datasets belonging to the movie and book domains. In addition to measuring the quality of the annotation process, we propose a novel metric to validate its cost-effectiveness. Finally, the practicality of our approach is further validated by training, evaluating, and determining the optimal configurations of several different kinds of machine-learning models on a range of sentiment classification tasks using the produced dataset.},\n number = {11},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: sr\nLanguage family: Indo-European\nGenus: Slavic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 5\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: other\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: sv\nLanguage family: Indo-European\nGenus: Germanic\nDefinite articles: definite affix\nIndefinite articles: indefinite word same as one\nNumber of cases: 2\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: interrogative word order\nPosition of negative word wrt SOV: more than one position\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: common, neuter\n\n@article{dataset_twitter_sentiment,\n doi = {10.1371/journal.pone.0155036},\n author = {Mozetič, Igor AND Grčar, Miha AND Smailović, Jasmina},\n journal = {PLOS ONE},\n publisher = {Public Library of Science},\n title = {Multilingual Twitter Sentiment Classification: The Role of Human Annotators},\n year = {2016},\n month = {05},\n volume = {11},\n url = {https://doi.org/10.1371/journal.pone.0155036},\n pages = {1-26},\n number = {5},\n}\n\n\n\n\n\nDomain: social_media\nLanguage: th\nLanguage family: Tai-Kadai\nGenus: Kam-Tai\nDefinite articles: no article\nIndefinite articles: indefinite word distinct from one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative auxiliary verb\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: little affixation\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: noun classifiers\n\n@misc{dataset_th_wisesight_sentiment,\n author = {Suriyawongkul, Arthit and\n Chuangsuwanich, Ekapol and\n Chormai, Pattarawat and\n Polpanumas, Charin},\n title = {PyThaiNLP/wisesight-sentiment: First release (v1.0)},\n month = sep,\n year = 2019,\n publisher = {Zenodo},\n version = {v1.0},\n doi = {10.5281/zenodo.3457447},\n url = {https://doi.org/10.5281/zenodo.3457447},\n note = {Zenodo}\n}\n\n\n\n\n\nDomain: reviews\nLanguage: th\nLanguage family: Tai-Kadai\nGenus: Kam-Tai\nDefinite articles: no article\nIndefinite articles: indefinite word distinct from one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative auxiliary verb\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: little affixation\nCoding of nominal plurality: mixed morphological plural\nGrammatical genders: noun classifiers\n\n@misc{dataset_th_wongnai_reviews,\n author = {Ekkalak Thongthanomkul and Tanapol Nearunchorn and Yuwat Chuesathuchon},\n title = {wongnai-corpus},\n year = {2019},\n publisher = {GitHub},\n journal = {GitHub repository},\n howpublished = {\\url{https://github.com/wongnai/wongnai-corpus}}\n}\n\n\n\n\n\nDomain: mixed\nLanguage: ur\nLanguage family: Indo-European\nGenus: Indic\nDefinite articles: no article\nIndefinite articles: no article\nNumber of cases: 2\nOrder of subject, object, verb: SOV\nNegative morphemes: negative affix\nPolar questions: question particle\nPosition of negative word wrt SOV: SONegV\nPrefixing vs suffixing: strongly suffixing\nCoding of nominal plurality: plural suffix\nGrammatical genders: masculine, feminine\n\n@InProceedings{dataset_ur_roman_urdu,\n title = \"Performing Natural Language Processing on Roman Urdu Datasets\",\n author = \"Zareen Sharf and Saif Ur Rahman\",\n booktitle = \"International Journal of Computer Science and Network Security\",\n volume = \"18\",\n pages = \"141-148\",\n year = \"2018\",\n url = {http://paper.ijcsns.org/07_book/201801/20180117.pdf}\n}\n\n\n\n\n\nDomain: reviews\nLanguage: zh\nLanguage family: Sino-Tibetan\nGenus: Chinese\nDefinite articles: no article\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: little affixation\nCoding of nominal plurality: no plural\nGrammatical genders: noun classifiers\n\n@inproceedings{dataset_zh_hotel_reviews,\n title = \"An Empirical Study on Sentiment Classification of {C}hinese Review using Word Embedding\",\n author = \"Lin, Yiou and\n Lei, Hang and\n Wu, Jia and\n Li, Xiaoyu\",\n booktitle = \"Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters\",\n month = oct,\n year = \"2015\",\n address = \"Shanghai, China\",\n url = \"https://aclanthology.org/Y15-2030\",\n pages = \"258--266\",\n}\n\n\n\n\nDomain: reviews\nLanguage: zh\nLanguage family: Sino-Tibetan\nGenus: Chinese\nDefinite articles: no article\nIndefinite articles: indefinite word same as one\nNumber of cases: no morphological case-making\nOrder of subject, object, verb: SVO\nNegative morphemes: negative particle\nPolar questions: question particle\nPosition of negative word wrt SOV: SNegVO\nPrefixing vs suffixing: little affixation\nCoding of nominal plurality: no plural\nGrammatical genders: noun classifiers\n\n@inproceedings{dataset_multilan_amazon,\n title = \"The Multilingual {A}mazon Reviews Corpus\",\n author = {Keung, Phillip and\n Lu, Yichao and\n Szarvas, Gy{\\\"o}rgy and\n Smith, Noah A.},\n booktitle = \"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)\",\n month = nov,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2020.emnlp-main.369\",\n doi = \"10.18653/v1/2020.emnlp-main.369\",\n pages = \"4563--4568\",\n}" + }, + { + "objectID": "index.html", + "href": "index.html", + "title": "MMS Dataset and Benchmark", + "section": "", + "text": "Despite impressive advancements in multilingual corpora collection and model training, developing large-scale deployments of multilingual models still presents a significant challenge. This is particularly true for language tasks that are culture-dependent. One such example is the area of multilingual sentiment analysis, where affective markers can be subtle and deeply ensconced in culture.\nThis work presents the most extensive open massively multilingual corpus of datasets for training sentiment models. The corpus consists of 79 manually selected datasets from over 350 datasets reported in the scientific literature based on strict quality criteria. The corpus covers 27 languages representing 6 language families. Datasets can be queried using several linguistic and functional features. In addition, we present a multi-faceted sentiment classification benchmark summarizing hundreds of experiments conducted on different base models, training objectives, dataset collections, and fine-tuning strategies." + }, + { + "objectID": "index.html#dataset", + "href": "index.html#dataset", + "title": "MMS Dataset and Benchmark", + "section": "Dataset", + "text": "Dataset\nMassively Multilingual Sentiment Datasets" + }, + { + "objectID": "index.html#analysis-and-benchmarking", + "href": "index.html#analysis-and-benchmarking", + "title": "MMS Dataset and Benchmark", + "section": "Analysis and benchmarking", + "text": "Analysis and benchmarking\nHuggingFace Spaces with Analysis and Benchmark" + }, + { + "objectID": "index.html#general-statistics-about-the-dataset", + "href": "index.html#general-statistics-about-the-dataset", + "title": "MMS Dataset and Benchmark", + "section": "General statistics about the dataset", + "text": "General statistics about the dataset\n\nIt may take some time to download the dataset and generate train set inside HuggingFace dataset. Please be patient.\n\n\nmms_dataset = datasets.load_dataset(\"Brand24/mms\")\n\n\nmms_dataset_df = mms_dataset[\"train\"].to_pandas()\n\nHow many examples do we have?\n\nmms_dataset.num_rows\n\n{'train': 6164762}" + }, + { + "objectID": "index.html#features", + "href": "index.html#features", + "title": "MMS Dataset and Benchmark", + "section": "Features", + "text": "Features\nWe provide not only texts and sentiment labels but we assigned many additional dimensions for datasets and languages, hence it is possible to splice and dice them as you want and need.\n\nmms_dataset[\"train\"].features\n\n{'_id': Value(dtype='int32', id=None),\n 'text': Value(dtype='string', id=None),\n 'label': ClassLabel(names=['negative', 'neutral', 'positive'], id=None),\n 'original_dataset': Value(dtype='string', id=None),\n 'domain': Value(dtype='string', id=None),\n 'language': Value(dtype='string', id=None),\n 'Family': Value(dtype='string', id=None),\n 'Genus': Value(dtype='string', id=None),\n 'Definite articles': Value(dtype='string', id=None),\n 'Indefinite articles': Value(dtype='string', id=None),\n 'Number of cases': Value(dtype='string', id=None),\n 'Order of subject, object, verb': Value(dtype='string', id=None),\n 'Negative morphemes': Value(dtype='string', id=None),\n 'Polar questions': Value(dtype='string', id=None),\n 'Position of negative word wrt SOV': Value(dtype='string', id=None),\n 'Prefixing vs suffixing': Value(dtype='string', id=None),\n 'Coding of nominal plurality': Value(dtype='string', id=None),\n 'Grammatical genders': Value(dtype='string', id=None),\n 'cleanlab_self_confidence': Value(dtype='float32', id=None)}\n\n\n\nExample\n\nmms_dataset[\"train\"][2001000]\n\n{'_id': 2001000,\n 'text': 'I was a tomboy and this has such great memories for me. They fit exactly how I remember, PERFECTLY!!',\n 'label': 2,\n 'original_dataset': 'en_amazon',\n 'domain': 'reviews',\n 'language': 'en',\n 'Family': 'Indo-European',\n 'Genus': 'Germanic',\n 'Definite articles': 'definite word distinct from demonstrative',\n 'Indefinite articles': 'indefinite word distinct from one',\n 'Number of cases': '2',\n 'Order of subject, object, verb': 'SVO',\n 'Negative morphemes': 'negative particle',\n 'Polar questions': 'interrogative word order',\n 'Position of negative word wrt SOV': 'SNegVO',\n 'Prefixing vs suffixing': 'strongly suffixing',\n 'Coding of nominal plurality': 'plural suffix',\n 'Grammatical genders': 'no grammatical gender',\n 'cleanlab_self_confidence': 0.9978116750717163}\n\n\n\n\nClasses\n\nlabels = mms_dataset[\"train\"].features[\"label\"].names\nlabels\n\n['negative', 'neutral', 'positive']\n\n\n\nmms_dataset_df[\"label_name\"] = mms_dataset_df[\"label\"].apply(lambda x: labels[x])\n\n\n\nClasses distribution\n\nlabels_stats_df = pd.DataFrame(mms_dataset_df.label_name.value_counts())\nlabels_stats_df[\"percentage\"] = (labels_stats_df[\"label_name\"] / labels_stats_df[\"label_name\"].sum()).round(3)\nlabels_stats_df\n\n\n\n\n\n\n\n\nlabel_name\npercentage\n\n\n\n\npositive\n3494478\n0.567\n\n\nneutral\n1341354\n0.218\n\n\nnegative\n1328930\n0.216" + }, + { + "objectID": "index.html#sentiment-orientation-for-each-language", + "href": "index.html#sentiment-orientation-for-each-language", + "title": "MMS Dataset and Benchmark", + "section": "Sentiment orientation for each language", + "text": "Sentiment orientation for each language\n\ncols = ['language', 'label_name']\nmms_dataset_df[cols].value_counts().to_frame().reset_index().rename(columns={0: 'count'}).sort_values(by=cols, ascending=True)\n\n\n\n\n\n\n\n\nlanguage\nlabel_name\ncount\n\n\n\n\n7\nar\nnegative\n138899\n\n\n4\nar\nneutral\n192774\n\n\n1\nar\npositive\n600402\n\n\n53\nbg\nnegative\n13930\n\n\n41\nbg\nneutral\n28657\n\n\n...\n...\n...\n...\n\n\n62\nur\nneutral\n8585\n\n\n67\nur\npositive\n5836\n\n\n9\nzh\nnegative\n117967\n\n\n21\nzh\nneutral\n69016\n\n\n6\nzh\npositive\n144719\n\n\n\n\n81 rows × 3 columns" + }, + { + "objectID": "index.html#per-language", + "href": "index.html#per-language", + "title": "MMS Dataset and Benchmark", + "section": "Per language", + "text": "Per language\n\ncols = ['language']\nmms_dataset_df[cols].value_counts().to_frame().reset_index().rename(columns={0: 'count'}).sort_values(by=cols, ascending=True)\n\n\n\n\n\n\n\n\nlanguage\ncount\n\n\n\n\n1\nar\n932075\n\n\n15\nbg\n62150\n\n\n20\nbs\n36183\n\n\n8\ncs\n196287\n\n\n4\nde\n315887\n\n\n0\nen\n2330486\n\n\n2\nes\n418712\n\n\n23\nfa\n13525\n\n\n6\nfr\n210631\n\n\n25\nhe\n8619\n\n\n22\nhi\n16999\n\n\n12\nhr\n77594\n\n\n16\nhu\n56682\n\n\n24\nit\n12065\n\n\n7\nja\n209780\n\n\n26\nlv\n5790\n\n\n5\npl\n236688\n\n\n9\npt\n157834\n\n\n11\nru\n110930\n\n\n17\nsk\n56623\n\n\n10\nsl\n113543\n\n\n18\nsq\n44284\n\n\n13\nsr\n76368\n\n\n19\nsv\n41346\n\n\n14\nth\n72319\n\n\n21\nur\n19660\n\n\n3\nzh\n331702" + }, + { + "objectID": "index.html#example-of-filtering-datasets", + "href": "index.html#example-of-filtering-datasets", + "title": "MMS Dataset and Benchmark", + "section": "Example of filtering datasets", + "text": "Example of filtering datasets\n\nChoose only Polish\n\npl = mms_dataset.filter(lambda row: row['language'] == 'pl')\n\n\n\n\n\npl[\"train\"].to_pandas().sample(5)\n\n\n\n\n\n\n\n\n_id\ntext\nlabel\noriginal_dataset\ndomain\nlanguage\nFamily\nGenus\nDefinite articles\nIndefinite articles\nNumber of cases\nOrder of subject, object, verb\nNegative morphemes\nPolar questions\nPosition of negative word wrt SOV\nPrefixing vs suffixing\nCoding of nominal plurality\nGrammatical genders\ncleanlab_self_confidence\n\n\n\n\n215921\n5119386\nTypujcie jaki dziś będzie wynik St.Pats - Legi...\n2\npl_twitter_sentiment\nsocial_media\npl\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n0.589098\n\n\n86525\n4989990\n@KaczmarSF Przyjemne ciarki mam, gdy patrzę na...\n2\npl_twitter_sentiment\nsocial_media\npl\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n0.950756\n\n\n66031\n4969496\nszkoda bylo czasu i kasy .\n0\npl_polemo\nreviews\npl\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n0.940540\n\n\n137768\n5041233\n@shinyvalentine mam ja w dupie lecz bylo to kr...\n0\npl_twitter_sentiment\nsocial_media\npl\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n0.220028\n\n\n118766\n5022231\n@itiNieWracaj pokazują to gdzieś?\n2\npl_twitter_sentiment\nsocial_media\npl\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n0.139179" + }, + { + "objectID": "index.html#use-cases", + "href": "index.html#use-cases", + "title": "MMS Dataset and Benchmark", + "section": "Use cases", + "text": "Use cases\n\nCase 1\nThus, when training a sentiment classifier using our dataset, one may download different facets of the collection. For instance, one can download all datasets in Slavic languages in which polar questions are formed using the interrogative word order or download all datasets from the Afro-Asiatic language family with no morphological case-making.\n\nslavic = mms_dataset.filter(lambda row: row[\"Genus\"] == \"Slavic\" and row[\"Polar questions\"] == \"interrogative word order\")\n\n\n\n\n\nslavic\n\nDatasetDict({\n train: Dataset({\n features: ['_id', 'text', 'label', 'original_dataset', 'domain', 'language', 'Family', 'Genus', 'Definite articles', 'Indefinite articles', 'Number of cases', 'Order of subject, object, verb', 'Negative morphemes', 'Polar questions', 'Position of negative word wrt SOV', 'Prefixing vs suffixing', 'Coding of nominal plurality', 'Grammatical genders', 'cleanlab_self_confidence'],\n num_rows: 252910\n })\n})\n\n\n\n\nCase 2\n\nafro_asiatic = mms_dataset.filter(lambda row: row[\"Family\"] == \"Afro-Asiatic\" and row[\"Number of cases\"] == \"no morphological case-making\")\n\n\n\n\n\nafro_asiatic\n\nDatasetDict({\n train: Dataset({\n features: ['_id', 'text', 'label', 'original_dataset', 'domain', 'language', 'Family', 'Genus', 'Definite articles', 'Indefinite articles', 'Number of cases', 'Order of subject, object, verb', 'Negative morphemes', 'Polar questions', 'Position of negative word wrt SOV', 'Prefixing vs suffixing', 'Coding of nominal plurality', 'Grammatical genders', 'cleanlab_self_confidence'],\n num_rows: 8619\n })\n})" + }, + { + "objectID": "index.html#dataset-curators", + "href": "index.html#dataset-curators", + "title": "MMS Dataset and Benchmark", + "section": "Dataset Curators", + "text": "Dataset Curators\nThe corpus was put together by\n\n@laugustyniak\n@swozniak\n@mgruza\n@pgramacki\n@krajda\n@mmorzy\n@tkajdanowicz" + }, + { + "objectID": "index.html#citation", + "href": "index.html#citation", + "title": "MMS Dataset and Benchmark", + "section": "Citation", + "text": "Citation\n@misc{augustyniak2023massively,\n title={Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmark}, \n author={Łukasz Augustyniak and Szymon Woźniak and Marcin Gruza and Piotr Gramacki and Krzysztof Rajda and Mikołaj Morzy and Tomasz Kajdanowicz},\n year={2023},\n eprint={2306.07902},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}" + }, + { + "objectID": "index.html#acknowledgements", + "href": "index.html#acknowledgements", + "title": "MMS Dataset and Benchmark", + "section": "Acknowledgements", + "text": "Acknowledgements\n\nBRAND24 - https://brand24.com\nCLARIN-PL-Biz - https://clarin.biz" + }, + { + "objectID": "index.html#licensing-information", + "href": "index.html#licensing-information", + "title": "MMS Dataset and Benchmark", + "section": "Licensing Information", + "text": "Licensing Information\nThese data are released under this licensing scheme. We do not own any text from which these data and datasets have been extracted.\nWe license the actual packaging of these data under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommons.org/licenses/by-nc/4.0/\nThis work is published from Poland.\nShould you consider that our data contains material that is owned by you and should, therefore not be reproduced here, please: * Clearly identify yourself with detailed contact data such as an address, telephone number, or email address at which you can be contacted. * Clearly identify the copyrighted work claimed to be infringed. * Clearly identify the material claimed to be infringing and the information reasonably sufficient to allow us to locate the material.\nWe will comply with legitimate requests by removing the affected sources from the next release of the corpus." + }, + { + "objectID": "benchmark_results.html", + "href": "benchmark_results.html", + "title": "Benchmark results", + "section": "", + "text": "Our preliminary results has been presented in (Rajda et al. 2022) and finally presented in (Augustyniak et al. 2023) review at NeurIPS’23." + }, + { + "objectID": "benchmark_results.html#benchmark-results---f1-macro-scores", + "href": "benchmark_results.html#benchmark-results---f1-macro-scores", + "title": "Benchmark results", + "section": "Benchmark results - F1 Macro scores", + "text": "Benchmark results - F1 Macro scores\n\nModels\n\n\n\n\n\n\n\n\n\n\n\n\nModel\nInf. time [s]\n#params\n#langs\nbase\ndata\nreference\n\n\n\n\nmT5\n1.69\n277M\n101\nT5\n\\(CC^b\\)\n(Xue et al. 2021)\n\n\nLASER\n1.64\n52M\n93\nBiLSTM\n\\(OPUS^c\\)\n(Artetxe and Schwenk 2019)\n\n\nmBERT\n1.49\n177M\n104\nBERT\nWiki\n(Devlin et al. 2019)\n\n\nMPNet**\n1.38\n278M\n53\nXLM-R\n\\(OPUS^c\\), \\(MUSE^d\\), \\(Wikititles^e\\)\n(Reimers and Gurevych 2020)\n\n\nXLM-R-dist**\n1.37\n278M\n53\nXLM-R\n\\(OPUS^c\\), \\(MUSE^d\\), \\(Wikititles^e\\)\n(Reimers and Gurevych 2020)\n\n\nXLM-R\n1.37\n278M\n100\nXLM-R\nCC\n(Conneau et al. 2020)\n\n\nLaBSE\n1.36\n470M\n109\nBERT\nCC, Wiki + mined bitexts\n(Feng et al. 2020)\n\n\nDistilmBERT\n0.79\n134M\n104\nBERT\nWiki\n(Sanh et al. 2020)\n\n\nmUSE-dist**\n0.79\n134M\n53\nDistilmBERT\n\\(OPUS^c\\), \\(MUSE^d\\), \\(Wikititles^e\\)\n(Reimers and Gurevych 2020)\n\n\nmUSE-transformer*\n0.65\n85M\n16\ntransformer\nmined QA + bitexts, SNLI\n(Yang et al. 2020)\n\n\nmUSE-cnn*\n0.12\n68M\n16\nCNN\nmined QA + bitexts, SNLI\n(Yang et al. 2020)\n\n\n\n\n* mUSE models were used in TensorFlow implementation in contrast to others in torch\na Base model is either monolingual version on which it was based or another multilingual model which was used and adopted\nb Colossal Clean Crawled Corpus in multilingual version (mC4)\nc multiple datasets from OPUS website (https://opus.nlpl.eu)\nd bilingual dictionaries from MUSE (https://github.com/facebookresearch/MUSE)\ne just titles from wiki articles in multiple languages\n\n\n\nResults" + }, + { + "objectID": "dataset_card.html", + "href": "dataset_card.html", + "title": "MMS Dataset Card", + "section": "", + "text": "One of the key ideas behind creating our library of datasets was to prioritize ease of use for researchers. Recognizing the importance of accessibility and convenience, we chose the HuggingFace platform as the storage and distribution platform for the datasets. HuggingFace provides a user-friendly interface and a wide range of tools and resources, making it easy for researchers to access and utilize the datasets.\nTo further enhance usability, we took the initiative to gather all the necessary citations for the datasets included in our library. By unifying the citations, we aimed to simplify and expedite the process of generating citations for researchers who utilize our datasets. This step reduces the time and effort required for researchers to acknowledge the datasets’ sources properly.\nHowever, it is essential to note that while we have taken steps to streamline the citation process, researchers should still independently verify the licenses of the datasets, especially if they intend to use them for purposes beyond strict academic research. Ensuring compliance with licensing requirements is crucial to maintaining ethical and legal data use standards.\nOverall, our overarching goal in creating this unified corpus of datasets is accelerating academic sentiment analysis research. By providing a comprehensive collection of high-quality datasets and facilitating their accessibility, we aim to support researchers in exploring and advancing sentiment analysis techniques and methodologies.\n\n\nOur dataset is designed to be versatile and allows researchers to slice and dice the data for training and modeling according to their specific needs. Drawing from the field of linguistic typology, which examines the characteristics of languages, we have incorporated various linguistic features into our dataset selection process. These features include the text itself, sentiment labels, the original dataset source, domain, language, language family, genus, the presence or absence of definite and indefinite articles, the number of cases, word order, negative morphemes, polar questions, the position of negative morphemes, prefixing vs. suffixing, coding of nominal plurals, and grammatical genders. Researchers can easily access datasets that match their desired linguistic typology criteria by offering these features as filtering options in our library.\nFor instance, researchers can download datasets specific to Slavic languages with interrogative word order for polar questions or datasets from the Afro-Asiatic language family without morphological case-making. This flexibility empowers researchers to tailor their analyses and models to their linguistic interests and research questions.\n\nimport datasets\n\nmms_dataset = datasets.load_dataset(\"Brand24/mms\")\nmms_dataset_df = mms_dataset[\"train\"].to_pandas()\n\nAll features in dataset\n\nmms_dataset_df.sample(5)\n\n\n\n\n\n\n\n\n_id\ntext\nlabel\noriginal_dataset\ndomain\nlanguage\nFamily\nGenus\nDefinite articles\nIndefinite articles\nNumber of cases\nOrder of subject, object, verb\nNegative morphemes\nPolar questions\nPosition of negative word wrt SOV\nPrefixing vs suffixing\nCoding of nominal plurality\nGrammatical genders\ncleanlab_self_confidence\n\n\n\n\n1117023\n1117023\nhlucnost mi prijde uplne v pohode, pere dobre,...\n2\ncs_mall_product_reviews\nreviews\ncs\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative affix\ninterrogative word order\nMorphNeg\nweakly suffixing\nplural suffix\nmasculine, feminine, neuter\n0.679376\n\n\n824580\n824580\n“فندق جميل ولكن الخدمة جدا سيئه”. . الخدمة غير...\n0\nar_hard\nreviews\nar\nAfro-Asiatic\nSemitic\ndefinite affix\nno article\n3\nSVO\nnegative particle\ninterrogative intonation only\nSNegVO\nweakly suffixing\nmixed morphological plural\nmasculine, feminine\n0.725264\n\n\n6014593\n6014593\n刚开始不习惯…之后还挺好用的…很轻便 很细…调节长度也很方便\n2\nzh_multilan_amazon\nreviews\nzh\nSino-Tibetan\nChinese\nno article\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\nquestion particle\nSNegVO\nlittle affixation\nno plural\nnoun classifiers\n0.907645\n\n\n5313872\n5313872\nЧемпионы. И этим все сказано.\n2\nru_sentiment\nsocial_media\nru\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n0.109386\n\n\n4290632\n4290632\n“@UnCharroDice: Y no ha de sobrar, quien con c...\n1\nes_twitter_sentiment\nsocial_media\nes\nIndo-European\nRomance\ndefinite word distinct from demonstrative\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine\n0.164549\n\n\n\n\n\n\n\n\n\n\nThe field of language typology focuses on studying the similarities and differences among languages. These differences can be categorized into phonological (sounds), syntactic (structures), lexical (vocabulary), and theoretical aspects. Linguistic typology analyzes the current state of languages, contrasting with genealogical linguistics, which examines historical relationships between languages.\nGenealogical linguistics studies language families and genera. A language family consists of languages that share a common ancestral language, while genera are branches within a language family. The Indo-European family, for example, includes genera such as Slavic, Romance, Germanic, and Indic. Over 7000 languages are categorized into approximately 150 language families, with Indo-European, Sino-Tibetan, Turkic, Afro-Asiatic, Nilo-Saharan, Niger-Congo, and Eskimo-Aleut being some of the largest families.\nWithin linguistic typology, languages are described using various linguistic features. Our work focuses on sentiment classification and selects ten relevant features:\n\ntext: The feature text represents the actual text of the sentiment dataset. It is of type string and contains the text samples or sentences for sentiment analysis.\nlabel: The feature label corresponds to the sentiment labels of the text samples. It is of type ClassLabel and has three possible values: negative, neutral, and positive. These labels indicate the sentiment or emotional polarity associated with the text.\noriginal_dataset: The feature original_dataset refers to the name or identifier of the original dataset from which the text samples were extracted. It is of type string and provides information about the source dataset.\ndomain: The feature domain represents the domain or topic of the sentiment dataset. It is of type string and provides context regarding the subject matter of the text samples.\nlanguage: The feature language indicates the language of the text samples in the sentiment dataset. It is of type string and specifies the language in which the text is written.\nFamily: The feature Family represents the language family to which a specific language belongs. It is of type string and provides information about the broader categorization of languages into language families.\nGenus: The feature Genus corresponds to the genus or branch within a language family. It is of type string and indicates the specific subgrouping of languages within a language family.\nDefinite article: Half of the languages do not use the definite article, which signals uniqueness or definiteness of a concept.\nIndefinite article: Half of the languages do not use the indefinite article, with some languages using a separate article or the numeral “one.”\nNumber of cases: Languages vary greatly in the number of morphological cases used.\nOrder of subject, verb, and object: Different languages have different word orderings, with variations like SOV, SVO, VSO, VOS, OVS, and OSV.\nNegative morphemes: Negative morphemes indicate clausal negation in declarative sentences.\nPolar questions: Questions with yes/no answers, which can be formed using question particles, interrogative morphology, or intonation.\nPosition of the negative morpheme: The position of the negative morpheme can vary in relation to subjects and objects.\nPrefixing vs. suffixing: Languages differ in their use of prefixes and suffixes in inflectional morphology.\nCoding of nominal plurals: Plurals can be expressed through morphological changes or the use of plurality indicator morphemes.\nGrammatical genders: Languages vary in the number of grammatical genders used, or may not use the concept at all.\n\nThese language features are available as filtering options in our library. Users can download specific facets of the collection, such as datasets in Slavic languages with interrogative word order for polar questions or datasets from the Afro-Asiatic language family without morphological case-making." + }, + { + "objectID": "dataset_card.html#easiness-of-using", + "href": "dataset_card.html#easiness-of-using", + "title": "MMS Dataset Card", + "section": "", + "text": "One of the key ideas behind creating our library of datasets was to prioritize ease of use for researchers. Recognizing the importance of accessibility and convenience, we chose the HuggingFace platform as the storage and distribution platform for the datasets. HuggingFace provides a user-friendly interface and a wide range of tools and resources, making it easy for researchers to access and utilize the datasets.\nTo further enhance usability, we took the initiative to gather all the necessary citations for the datasets included in our library. By unifying the citations, we aimed to simplify and expedite the process of generating citations for researchers who utilize our datasets. This step reduces the time and effort required for researchers to acknowledge the datasets’ sources properly.\nHowever, it is essential to note that while we have taken steps to streamline the citation process, researchers should still independently verify the licenses of the datasets, especially if they intend to use them for purposes beyond strict academic research. Ensuring compliance with licensing requirements is crucial to maintaining ethical and legal data use standards.\nOverall, our overarching goal in creating this unified corpus of datasets is accelerating academic sentiment analysis research. By providing a comprehensive collection of high-quality datasets and facilitating their accessibility, we aim to support researchers in exploring and advancing sentiment analysis techniques and methodologies.\n\n\nOur dataset is designed to be versatile and allows researchers to slice and dice the data for training and modeling according to their specific needs. Drawing from the field of linguistic typology, which examines the characteristics of languages, we have incorporated various linguistic features into our dataset selection process. These features include the text itself, sentiment labels, the original dataset source, domain, language, language family, genus, the presence or absence of definite and indefinite articles, the number of cases, word order, negative morphemes, polar questions, the position of negative morphemes, prefixing vs. suffixing, coding of nominal plurals, and grammatical genders. Researchers can easily access datasets that match their desired linguistic typology criteria by offering these features as filtering options in our library.\nFor instance, researchers can download datasets specific to Slavic languages with interrogative word order for polar questions or datasets from the Afro-Asiatic language family without morphological case-making. This flexibility empowers researchers to tailor their analyses and models to their linguistic interests and research questions.\n\nimport datasets\n\nmms_dataset = datasets.load_dataset(\"Brand24/mms\")\nmms_dataset_df = mms_dataset[\"train\"].to_pandas()\n\nAll features in dataset\n\nmms_dataset_df.sample(5)\n\n\n\n\n\n\n\n\n_id\ntext\nlabel\noriginal_dataset\ndomain\nlanguage\nFamily\nGenus\nDefinite articles\nIndefinite articles\nNumber of cases\nOrder of subject, object, verb\nNegative morphemes\nPolar questions\nPosition of negative word wrt SOV\nPrefixing vs suffixing\nCoding of nominal plurality\nGrammatical genders\ncleanlab_self_confidence\n\n\n\n\n1117023\n1117023\nhlucnost mi prijde uplne v pohode, pere dobre,...\n2\ncs_mall_product_reviews\nreviews\ncs\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative affix\ninterrogative word order\nMorphNeg\nweakly suffixing\nplural suffix\nmasculine, feminine, neuter\n0.679376\n\n\n824580\n824580\n“فندق جميل ولكن الخدمة جدا سيئه”. . الخدمة غير...\n0\nar_hard\nreviews\nar\nAfro-Asiatic\nSemitic\ndefinite affix\nno article\n3\nSVO\nnegative particle\ninterrogative intonation only\nSNegVO\nweakly suffixing\nmixed morphological plural\nmasculine, feminine\n0.725264\n\n\n6014593\n6014593\n刚开始不习惯…之后还挺好用的…很轻便 很细…调节长度也很方便\n2\nzh_multilan_amazon\nreviews\nzh\nSino-Tibetan\nChinese\nno article\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\nquestion particle\nSNegVO\nlittle affixation\nno plural\nnoun classifiers\n0.907645\n\n\n5313872\n5313872\nЧемпионы. И этим все сказано.\n2\nru_sentiment\nsocial_media\nru\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n0.109386\n\n\n4290632\n4290632\n“@UnCharroDice: Y no ha de sobrar, quien con c...\n1\nes_twitter_sentiment\nsocial_media\nes\nIndo-European\nRomance\ndefinite word distinct from demonstrative\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine\n0.164549\n\n\n\n\n\n\n\n\n\n\nThe field of language typology focuses on studying the similarities and differences among languages. These differences can be categorized into phonological (sounds), syntactic (structures), lexical (vocabulary), and theoretical aspects. Linguistic typology analyzes the current state of languages, contrasting with genealogical linguistics, which examines historical relationships between languages.\nGenealogical linguistics studies language families and genera. A language family consists of languages that share a common ancestral language, while genera are branches within a language family. The Indo-European family, for example, includes genera such as Slavic, Romance, Germanic, and Indic. Over 7000 languages are categorized into approximately 150 language families, with Indo-European, Sino-Tibetan, Turkic, Afro-Asiatic, Nilo-Saharan, Niger-Congo, and Eskimo-Aleut being some of the largest families.\nWithin linguistic typology, languages are described using various linguistic features. Our work focuses on sentiment classification and selects ten relevant features:\n\ntext: The feature text represents the actual text of the sentiment dataset. It is of type string and contains the text samples or sentences for sentiment analysis.\nlabel: The feature label corresponds to the sentiment labels of the text samples. It is of type ClassLabel and has three possible values: negative, neutral, and positive. These labels indicate the sentiment or emotional polarity associated with the text.\noriginal_dataset: The feature original_dataset refers to the name or identifier of the original dataset from which the text samples were extracted. It is of type string and provides information about the source dataset.\ndomain: The feature domain represents the domain or topic of the sentiment dataset. It is of type string and provides context regarding the subject matter of the text samples.\nlanguage: The feature language indicates the language of the text samples in the sentiment dataset. It is of type string and specifies the language in which the text is written.\nFamily: The feature Family represents the language family to which a specific language belongs. It is of type string and provides information about the broader categorization of languages into language families.\nGenus: The feature Genus corresponds to the genus or branch within a language family. It is of type string and indicates the specific subgrouping of languages within a language family.\nDefinite article: Half of the languages do not use the definite article, which signals uniqueness or definiteness of a concept.\nIndefinite article: Half of the languages do not use the indefinite article, with some languages using a separate article or the numeral “one.”\nNumber of cases: Languages vary greatly in the number of morphological cases used.\nOrder of subject, verb, and object: Different languages have different word orderings, with variations like SOV, SVO, VSO, VOS, OVS, and OSV.\nNegative morphemes: Negative morphemes indicate clausal negation in declarative sentences.\nPolar questions: Questions with yes/no answers, which can be formed using question particles, interrogative morphology, or intonation.\nPosition of the negative morpheme: The position of the negative morpheme can vary in relation to subjects and objects.\nPrefixing vs. suffixing: Languages differ in their use of prefixes and suffixes in inflectional morphology.\nCoding of nominal plurals: Plurals can be expressed through morphological changes or the use of plurality indicator morphemes.\nGrammatical genders: Languages vary in the number of grammatical genders used, or may not use the concept at all.\n\nThese language features are available as filtering options in our library. Users can download specific facets of the collection, such as datasets in Slavic languages with interrogative word order for polar questions or datasets from the Afro-Asiatic language family without morphological case-making." + }, + { + "objectID": "dataset_card.html#datasheets-for-datasets", + "href": "dataset_card.html#datasheets-for-datasets", + "title": "MMS Dataset Card", + "section": "Datasheets for Datasets", + "text": "Datasheets for Datasets\nThe datasheets provide detailed information about the datasets, including data collection methods, annotation guidelines, and potential biases. They also specify the intended uses and potential limitations of the datasets.\nThe initial pool of sentiment datasets was gathered through an extensive search using sources such as Google Scholar, GitHub repositories, and the HuggingFace datasets library. This search yielded a total of 345 datasets.\nTo ensure the quality of the datasets, a set of quality assurance criteria was applied to manually filter the initial pool of datasets. The following criteria were used:\n\nStrong Annotations: Datasets containing weak annotations, such as labels based on emoji occurrence or automatically generated through classification by machine learning models, were rejected. This decision was made to minimize the presence of noise in the datasets, ensuring higher quality annotations.\nWell-Defined Annotation Protocol: Datasets without sufficient information about the annotation protocol, including whether the annotation was done manually or automatically and the number of annotators involved, were rejected. This step aimed to avoid merging datasets with contradicting annotation instructions, ensuring consistency across the selected datasets.\nNumerical Ratings: Datasets with numerical ratings were accepted. Specifically, Likert-type 5-point scales were mapped into three class sentiment labels. Ratings 1 and 2 were mapped to “negative,” rating 3 was mapped to “neutral,” and ratings 4 and 5 were mapped to “positive.” This mapping allowed for consistent sentiment labeling across the datasets.\nThree Classes Only: Datasets annotated with binary sentiment labels were rejected. The decision to focus on datasets with three sentiment classes (negative, neutral, and positive) was made based on the unsatisfactory performance of binary sentiment labeling in three-class settings.\nMonolingual Datasets: In cases where a dataset contained samples in multiple languages, it was divided into independent datasets for each constituent language. This approach ensured that the corpus includes separate datasets for different languages, allowing for targeted analysis and evaluation.\n\nBy applying these quality assurance criteria, we were able to filter the initial pool of sentiment datasets and select a final set of 79 datasets that met the specified standards for inclusion in the multilingual corpus.\n\nf\"We cover {mms_dataset_df.original_dataset.nunique()} datasets in {mms_dataset_df.language.nunique()} languages.\"\n\n'We cover 79 datasets in 27 languages.'\n\n\n\nf\"The classes that we cover: {mms_dataset_df.label_name.unique()}\"\n\n\"The classes that we cover: ['positive' 'neutral' 'negative']\"" + }, + { + "objectID": "dataset_card.html#limitations", + "href": "dataset_card.html#limitations", + "title": "MMS Dataset Card", + "section": "Limitations", + "text": "Limitations\nDespite the fact that our collection is the largest public collection of multilingual sentiment datasets, it still covers only 27 languages. The collection of datasets is highly biased towards the Indo-European family of languages, English in particular. We attribute this bias to the general culture of scientific publishing and its enforcement of English as the primary carrier of scientific discovery. Our work’s main potential negative social impact is that the models developed and trained using the provided datasets may still exhibit better performance for the major languages. This could further perpetuate the existing language disparities and inequality in sentiment analysis capabilities across different languages. Addressing this limitation and working towards more equitable representation and performance across languages is crucial to avoid reinforcing language biases and the potential marginalization of underrepresented languages. The ethical implications of such disparities should be thoroughly discussed and considered.\n\n\n\nData Quality\n\n\nAn important limitation of our dataset collection is a significant variance in sample quality across all datasets and all languages. Above figure presents the distribution of self-confidence label-quality score for each data point computed by the cleanlab (Northcutt, Jiang, and Chuang 2021). The distribution of quality is skewed in favor of popular languages, with low-resource languages suffering from data quality issues. A related limitation is caused by an unequal distribution of data modalities across languages. For instance, our benchmark clearly shows that all models universally underperform when tested on Portuguese datasets. This is the direct result of the fact that data points for Portuguese almost exclusively represent the domain of social media. As a consequence, some combinations of filtering facets in our dataset collection produce very little data (i.e., asking for social media data in the Germanic genus of Indo-European languages will produce a significantly larger dataset than asking for news data representing Afro-Asiatic languages).\nFinally, we acknowledge the lack of internal coherence of annotation protocols between datasets and languages. We have enforced strict quality criteria and rejected all datasets published without the annotation protocol, but we were unable, for obvious reasons, to unify annotation guidelines. The annotation of sentiment expressions and the assignment of sentiment labels are heavily subjective and, at the same time, influenced by cultural and linguistic features. Unfortunately, it is possible that semantically similar utterances will be assigned conflicting labels if they come from different datasets or modalities.\n\nFilter examples by annotation qualitym\nWe know how imporant data quality is for the model training processes. Hence, we added cleanlab scores to each of 6M+ examples in all datasets. Now, it is enalbe to filter examples based on how good quality of data do you need for traning.\nWe can sort examples by top data quality. Cleanlab’s self confidence is a function to compute label-quality scores for classification datasets, where lower scores indicate labels less likely to be correct. Hence, for the best quality we want to have the highest scores.\n\nclean_labels_data = mms_dataset_df.sort_values(by=\"cleanlab_self_confidence\", ascending=False).head(10_000)\n\n\nclean_labels_data.head()\n\n\n\n\n\n\n\n\n_id\ntext\nlabel\noriginal_dataset\ndomain\nlanguage\nFamily\nGenus\nDefinite articles\nIndefinite articles\nNumber of cases\nOrder of subject, object, verb\nNegative morphemes\nPolar questions\nPosition of negative word wrt SOV\nPrefixing vs suffixing\nCoding of nominal plurality\nGrammatical genders\ncleanlab_self_confidence\nlabel_name\n\n\n\n\n3075302\n3075302\nGreat addition to any fan's yard! Show your te...\n2\nen_amazon\nreviews\nen\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n0.999981\npositive\n\n\n629922\n629922\nمخيب للأمل. . ىحَ\n0\nar_hard\nreviews\nar\nAfro-Asiatic\nSemitic\ndefinite affix\nno article\n3\nSVO\nnegative particle\ninterrogative intonation only\nSNegVO\nweakly suffixing\nmixed morphological plural\nmasculine, feminine\n0.999964\nnegative\n\n\n2858237\n2858237\nThis is a great flag to display your love of A...\n2\nen_amazon\nreviews\nen\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n0.999950\npositive\n\n\n3110031\n3110031\nOne of the best knives I now proudly own! Am a...\n2\nen_amazon\nreviews\nen\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n0.999950\npositive\n\n\n2052971\n2052971\nAmen! My Savior Loves! Wonderful testimony!\n2\nen_amazon\nreviews\nen\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n0.999948\npositive" + }, + { + "objectID": "dataset_card.html#datasets", + "href": "dataset_card.html#datasets", + "title": "MMS Dataset Card", + "section": "Datasets", + "text": "Datasets\nWe added all necessary citations to the HuggingFace datasets card. You can find them inside citation key. We added a helper fuinctions to parse them.\nWe can load citations as strings - easy adding to bibtex.\n\nfrom mms_benchmark.citations import get_citations\n\n\nprint(get_citations(mms_dataset[\"train\"], citation_as_dict=False)[\"pl_polemo\"])\n\n@inproceedings{dataset_pl_polemo,\n title = \"Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews\",\n author = \"Koco{\\'n}, Jan and\n Mi{\\l}kowski, Piotr and\n Za{\\'s}ko-Zieli{\\'n}ska, Monika\",\n booktitle = \"Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)\",\n month = nov,\n year = \"2019\",\n address = \"Hong Kong, China\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/K19-1092\",\n doi = \"10.18653/v1/K19-1092\",\n pages = \"980--991\"\n}\n% ------------------------------------------------------------------------------------------\n\n\n\nOr as dictionary for working with them.\n\ncitations = get_citations(mms_dataset[\"train\"], citation_as_dict=True)\n\n\ncitations[\"pl_polemo\"]\n\n{'pages': '980--991',\n 'doi': '10.18653/v1/K19-1092',\n 'url': 'https://aclanthology.org/K19-1092',\n 'publisher': 'Association for Computational Linguistics',\n 'address': 'Hong Kong, China',\n 'year': '2019',\n 'month': 'November',\n 'booktitle': 'Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)',\n 'author': \"Koco{\\\\'n}, Jan and\\nMi{\\\\l}kowski, Piotr and\\nZa{\\\\'s}ko-Zieli{\\\\'n}ska, Monika\",\n 'title': 'Multi-Level Sentiment Analysis of {P}ol{E}mo 2.0: Extended Corpus of Multi-Domain Consumer Reviews',\n 'ENTRYTYPE': 'inproceedings',\n 'ID': 'dataset_pl_polemo'}\n\n\n\nShow all datasets with citations in a table\n\nmms_dataset_df[\"citation\"] = mms_dataset_df[\"original_dataset\"].apply(lambda x: f'[@{citations[x][\"ID\"]}]')\n\n\nmms_dataset_df[DATASET_COLS].drop_duplicates().sort_values(\"language\").reset_index(drop=True)\n\n\n\n\n\n\n\n\nlanguage\noriginal_dataset\ndomain\nFamily\nGenus\nDefinite articles\nIndefinite articles\nNumber of cases\nOrder of subject, object, verb\nNegative morphemes\nPolar questions\nPosition of negative word wrt SOV\nPrefixing vs suffixing\nCoding of nominal plurality\nGrammatical genders\ncitation\n\n\n\n\n0\nar\nar_arsentdl\nsocial_media\nAfro-Asiatic\nSemitic\ndefinite affix\nno article\n3\nSVO\nnegative particle\ninterrogative intonation only\nSNegVO\nweakly suffixing\nmixed morphological plural\nmasculine, feminine\n[@dataset_ar_arsentdl]\n\n\n1\nar\nar_semeval_2017\nmixed\nAfro-Asiatic\nSemitic\ndefinite affix\nno article\n3\nSVO\nnegative particle\ninterrogative intonation only\nSNegVO\nweakly suffixing\nmixed morphological plural\nmasculine, feminine\n[@dataset_semeval_2017]\n\n\n2\nar\nar_oclar\nreviews\nAfro-Asiatic\nSemitic\ndefinite affix\nno article\n3\nSVO\nnegative particle\ninterrogative intonation only\nSNegVO\nweakly suffixing\nmixed morphological plural\nmasculine, feminine\n[@dataset_ar_oclar]\n\n\n3\nar\nar_labr\nreviews\nAfro-Asiatic\nSemitic\ndefinite affix\nno article\n3\nSVO\nnegative particle\ninterrogative intonation only\nSNegVO\nweakly suffixing\nmixed morphological plural\nmasculine, feminine\n[@dataset_ar_labr]\n\n\n4\nar\nar_syria_corpus\nsocial_media\nAfro-Asiatic\nSemitic\ndefinite affix\nno article\n3\nSVO\nnegative particle\ninterrogative intonation only\nSNegVO\nweakly suffixing\nmixed morphological plural\nmasculine, feminine\n[@dataset_ar_bbn]\n\n\n5\nar\nar_brad\nreviews\nAfro-Asiatic\nSemitic\ndefinite affix\nno article\n3\nSVO\nnegative particle\ninterrogative intonation only\nSNegVO\nweakly suffixing\nmixed morphological plural\nmasculine, feminine\n[@dataset_ar_brad]\n\n\n6\nar\nar_bbn\nsocial_media\nAfro-Asiatic\nSemitic\ndefinite affix\nno article\n3\nSVO\nnegative particle\ninterrogative intonation only\nSNegVO\nweakly suffixing\nmixed morphological plural\nmasculine, feminine\n[@dataset_ar_bbn]\n\n\n7\nar\nar_astd\nsocial_media\nAfro-Asiatic\nSemitic\ndefinite affix\nno article\n3\nSVO\nnegative particle\ninterrogative intonation only\nSNegVO\nweakly suffixing\nmixed morphological plural\nmasculine, feminine\n[@dataset_ar_astd]\n\n\n8\nar\nar_hard\nreviews\nAfro-Asiatic\nSemitic\ndefinite affix\nno article\n3\nSVO\nnegative particle\ninterrogative intonation only\nSNegVO\nweakly suffixing\nmixed morphological plural\nmasculine, feminine\n[@dataset_ar_hard]\n\n\n9\nbg\nbg_twitter_sentiment\nsocial_media\nIndo-European\nSlavic\ndefinite word distinct from demonstrative\nno article\nno morphological case-making\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_twitter_sentiment]\n\n\n10\nbs\nbs_twitter_sentiment\nsocial_media\nIndo-European\nSlavic\nno article\nno article\n5\nSVO\nnegative particle\nquestion particle\nother\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_twitter_sentiment]\n\n\n11\ncs\ncs_facebook\nsocial_media\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative affix\ninterrogative word order\nMorphNeg\nweakly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_cs_social_media]\n\n\n12\ncs\ncs_mall_product_reviews\nreviews\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative affix\ninterrogative word order\nMorphNeg\nweakly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_cs_social_media]\n\n\n13\ncs\ncs_movie_reviews\nreviews\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative affix\ninterrogative word order\nMorphNeg\nweakly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_cs_social_media]\n\n\n14\ncs\ncs_news_stance\nsocial_media\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative affix\ninterrogative word order\nMorphNeg\nweakly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_cs_social_media]\n\n\n15\nde\nde_twitter_sentiment\nsocial_media\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word same as one\n4\nno dominant order\nnegative particle\ninterrogative word order\nmore than one position\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_twitter_sentiment]\n\n\n16\nde\nde_omp\nsocial_media\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word same as one\n4\nno dominant order\nnegative particle\ninterrogative word order\nmore than one position\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_de_omp]\n\n\n17\nde\nde_sb10k\nsocial_media\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word same as one\n4\nno dominant order\nnegative particle\ninterrogative word order\nmore than one position\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_de_sb10k]\n\n\n18\nde\nde_ifeel\nsocial_media\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word same as one\n4\nno dominant order\nnegative particle\ninterrogative word order\nmore than one position\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_dai_labor]\n\n\n19\nde\nde_dai_labor\nsocial_media\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word same as one\n4\nno dominant order\nnegative particle\ninterrogative word order\nmore than one position\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_dai_labor]\n\n\n20\nde\nde_multilan_amazon\nreviews\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word same as one\n4\nno dominant order\nnegative particle\ninterrogative word order\nmore than one position\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_multilan_amazon]\n\n\n21\nen\nen_vader_twitter\nsocial_media\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_en_vader]\n\n\n22\nen\nen_vader_nyt\nnews\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_en_vader]\n\n\n23\nen\nen_vader_movie_reviews\nreviews\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_en_vader]\n\n\n24\nen\nen_vader_amazon\nreviews\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_en_vader]\n\n\n25\nen\nen_twitter_sentiment\nsocial_media\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_twitter_sentiment]\n\n\n26\nen\nen_tweets_sanders\nsocial_media\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_en_tweets_sanders]\n\n\n27\nen\nen_tweet_airlines\nsocial_media\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_en_tweet_airlines]\n\n\n28\nen\nen_silicone_sem\nchats\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_en_silicone]\n\n\n29\nen\nen_sentistrength\nsocial_media\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_en_sentistrength]\n\n\n30\nen\nen_semeval_2017\nmixed\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_semeval_2017]\n\n\n31\nen\nen_poem_sentiment\npoems\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_en_poem_sentiment]\n\n\n32\nen\nen_per_sent\nnews\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_en_per_sent]\n\n\n33\nen\nen_multilan_amazon\nreviews\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_multilan_amazon]\n\n\n34\nen\nen_financial_phrasebank_sentences_75agree\nnews\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_en_financial_phrasebank_sentences_75agree]\n\n\n35\nen\nen_dai_labor\nsocial_media\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_dai_labor]\n\n\n36\nen\nen_amazon\nreviews\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_en_amazon]\n\n\n37\nen\nen_silicone_meld_s\nchats\nIndo-European\nGermanic\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n2\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_en_silicone]\n\n\n38\nes\nes_twitter_sentiment\nsocial_media\nIndo-European\nRomance\ndefinite word distinct from demonstrative\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_twitter_sentiment]\n\n\n39\nes\nes_semeval2020\nsocial_media\nIndo-European\nRomance\ndefinite word distinct from demonstrative\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_semeval_2020]\n\n\n40\nes\nes_multilan_amazon\nreviews\nIndo-European\nRomance\ndefinite word distinct from demonstrative\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_multilan_amazon]\n\n\n41\nes\nes_muchocine\nreviews\nIndo-European\nRomance\ndefinite word distinct from demonstrative\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_es_muchocine]\n\n\n42\nes\nes_paper_reviews\nreviews\nIndo-European\nRomance\ndefinite word distinct from demonstrative\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\ninterrogative word order\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_es_paper_reviews]\n\n\n43\nfa\nfa_sentipers\nreviews\nIndo-European\nIranian\nno article\nindefinite word same as one\n2\nSOV\nnegative affix\nquestion particle\nMorphNeg\nweakly suffixing\nplural suffix\nno grammatical gender\n[@dataset_fa_sentipers]\n\n\n44\nfr\nfr_dai_labor\nsocial_media\nIndo-European\nRomance\ndefinite word distinct from demonstrative\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\nquestion particle\nOptDoubleNeg\nstrongly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_dai_labor]\n\n\n45\nfr\nfr_ifeel\nsocial_media\nIndo-European\nRomance\ndefinite word distinct from demonstrative\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\nquestion particle\nOptDoubleNeg\nstrongly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_dai_labor]\n\n\n46\nfr\nfr_multilan_amazon\nreviews\nIndo-European\nRomance\ndefinite word distinct from demonstrative\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\nquestion particle\nOptDoubleNeg\nstrongly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_multilan_amazon]\n\n\n47\nhe\nhe_hebrew_sentiment\nsocial_media\nAfro-Asiatic\nSemitic\ndefinite affix\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\nquestion particle\nSNegVO\nweakly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_he_hebrew_sentiment]\n\n\n48\nhi\nhi_semeval2020\nsocial_media\nIndo-European\nIndic\nno article\nno article\n3\nSOV\nnegative particle\nquestion particle\nSONegV\nstrongly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_semeval_2020]\n\n\n49\nhr\nhr_sentiment_news_document\nnews\nIndo-European\nSlavic\nno article\nno article\n5\nSVO\nnegative particle\nquestion particle\nother\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_hr_sentiment_news_document]\n\n\n50\nhr\nhr_twitter_sentiment\nsocial_media\nIndo-European\nSlavic\nno article\nno article\n5\nSVO\nnegative particle\nquestion particle\nother\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_twitter_sentiment]\n\n\n51\nhu\nhu_twitter_sentiment\nsocial_media\nUralic\nUgric\ndefinite word distinct from demonstrative\nindefinite word distinct from one\n10 or more\nno dominant order\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_twitter_sentiment]\n\n\n52\nit\nit_evalita2016\nsocial_media\nIndo-European\nRomance\ndefinite word distinct from demonstrative\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\ninterrogative intonation only\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_it_evalita2016]\n\n\n53\nit\nit_multiemotions\nsocial_media\nIndo-European\nRomance\ndefinite word distinct from demonstrative\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\ninterrogative intonation only\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_it_multiemotions]\n\n\n54\nja\nja_multilan_amazon\nreviews\nJapanese\nJapanese\nno article\nindefinite word distinct from one\n8-9\nSOV\nnegative affix\nquestion particle\nMorphNeg\nstrongly suffixing\nplural suffix\nno grammatical gender\n[@dataset_multilan_amazon]\n\n\n55\nlv\nlv_ltec_sentiment\nsocial_media\nIndo-European\nBaltic\ndemonstrative word used as definite article\nindefinite word same as one\n5\nSVO\nnegative affix\nquestion particle\nMorphNeg\nweakly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_lv_ltec_sentiment]\n\n\n56\npl\npl_twitter_sentiment\nsocial_media\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_twitter_sentiment]\n\n\n57\npl\npl_polemo\nreviews\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_pl_polemo]\n\n\n58\npl\npl_klej_allegro_reviews\nreviews\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_pl_klej_allegro_reviews]\n\n\n59\npl\npl_opi_lil_2012\nsocial_media\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_pl_opi_lil_2012]\n\n\n60\npt\npt_dai_labor\nsocial_media\nIndo-European\nRomance\ndefinite word distinct from demonstrative\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_dai_labor]\n\n\n61\npt\npt_ifeel\nsocial_media\nIndo-European\nRomance\ndefinite word distinct from demonstrative\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_dai_labor]\n\n\n62\npt\npt_tweet_sent_br\nsocial_media\nIndo-European\nRomance\ndefinite word distinct from demonstrative\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_pt_tweet_sent_br]\n\n\n63\npt\npt_twitter_sentiment\nsocial_media\nIndo-European\nRomance\ndefinite word distinct from demonstrative\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_twitter_sentiment]\n\n\n64\nru\nru_sentiment\nsocial_media\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_ru_sentiment]\n\n\n65\nru\nru_twitter_sentiment\nsocial_media\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_twitter_sentiment]\n\n\n66\nsk\nsk_twitter_sentiment\nsocial_media\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative affix\ninterrogative word order\nMorphNeg\nweakly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_twitter_sentiment]\n\n\n67\nsl\nsl_sentinews\nnews\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@Bučar2018]\n\n\n68\nsl\nsl_twitter_sentiment\nsocial_media\nIndo-European\nSlavic\nno article\nno article\n6-7\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_twitter_sentiment]\n\n\n69\nsq\nsq_twitter_sentiment\nsocial_media\nIndo-European\nAlbanian\ndefinite affix\nindefinite word distinct from one\n4\nSVO\nnegative particle\nquestion particle\nSNegVO\nstrongly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_twitter_sentiment]\n\n\n70\nsr\nsr_movie_reviews\nreviews\nIndo-European\nSlavic\nno article\nno article\n5\nSVO\nnegative particle\nquestion particle\nother\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_sr_serb_movie_reviews]\n\n\n71\nsr\nsr_senticomments\nreviews\nIndo-European\nSlavic\nno article\nno article\n5\nSVO\nnegative particle\nquestion particle\nother\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_sr_senticomments]\n\n\n72\nsr\nsr_twitter_sentiment\nsocial_media\nIndo-European\nSlavic\nno article\nno article\n5\nSVO\nnegative particle\nquestion particle\nother\nstrongly suffixing\nplural suffix\nmasculine, feminine, neuter\n[@dataset_twitter_sentiment]\n\n\n73\nsv\nsv_twitter_sentiment\nsocial_media\nIndo-European\nGermanic\ndefinite affix\nindefinite word same as one\n2\nSVO\nnegative particle\ninterrogative word order\nmore than one position\nstrongly suffixing\nplural suffix\ncommon, neuter\n[@dataset_twitter_sentiment]\n\n\n74\nth\nth_wongnai_reviews\nreviews\nTai-Kadai\nKam-Tai\nno article\nindefinite word distinct from one\nno morphological case-making\nSVO\nnegative auxiliary verb\nquestion particle\nSNegVO\nlittle affixation\nmixed morphological plural\nnoun classifiers\n[@dataset_th_wongnai_reviews]\n\n\n75\nth\nth_wisesight_sentiment\nsocial_media\nTai-Kadai\nKam-Tai\nno article\nindefinite word distinct from one\nno morphological case-making\nSVO\nnegative auxiliary verb\nquestion particle\nSNegVO\nlittle affixation\nmixed morphological plural\nnoun classifiers\n[@dataset_th_wisesight_sentiment]\n\n\n76\nur\nur_roman_urdu\nmixed\nIndo-European\nIndic\nno article\nno article\n2\nSOV\nnegative affix\nquestion particle\nSONegV\nstrongly suffixing\nplural suffix\nmasculine, feminine\n[@dataset_ur_roman_urdu]\n\n\n77\nzh\nzh_hotel_reviews\nreviews\nSino-Tibetan\nChinese\nno article\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\nquestion particle\nSNegVO\nlittle affixation\nno plural\nnoun classifiers\n[@dataset_zh_hotel_reviews]\n\n\n78\nzh\nzh_multilan_amazon\nreviews\nSino-Tibetan\nChinese\nno article\nindefinite word same as one\nno morphological case-making\nSVO\nnegative particle\nquestion particle\nSNegVO\nlittle affixation\nno plural\nnoun classifiers\n[@dataset_multilan_amazon]" + }, + { + "objectID": "dataset_card.html#dataset-stats", + "href": "dataset_card.html#dataset-stats", + "title": "MMS Dataset Card", + "section": "Dataset Stats", + "text": "Dataset Stats\n\nDatasets per language\n\npd.DataFrame(mms_dataset_df.groupby(\"language\").original_dataset.nunique().sort_values(ascending=False))\n\n\n\n\n\n\n\n\noriginal_dataset\n\n\nlanguage\n\n\n\n\n\nen\n17\n\n\nar\n9\n\n\nde\n6\n\n\nes\n5\n\n\npl\n4\n\n\ncs\n4\n\n\npt\n4\n\n\nsr\n3\n\n\nfr\n3\n\n\nth\n2\n\n\nsl\n2\n\n\nru\n2\n\n\nit\n2\n\n\nhr\n2\n\n\nzh\n2\n\n\nbg\n1\n\n\nja\n1\n\n\nlv\n1\n\n\nhu\n1\n\n\nhi\n1\n\n\nsk\n1\n\n\nhe\n1\n\n\nsq\n1\n\n\nfa\n1\n\n\nsv\n1\n\n\nbs\n1\n\n\nur\n1\n\n\n\n\n\n\n\n\n\nLabels per language\n\npd.DataFrame(mms_dataset_df.groupby(by=[\"language\", \"label_name\"]).count()[\"text\"])\n\n\n\n\n\n\n\n\n\ntext\n\n\nlanguage\nlabel_name\n\n\n\n\n\nar\nnegative\n138899\n\n\nneutral\n192774\n\n\npositive\n600402\n\n\nbg\nnegative\n13930\n\n\nneutral\n28657\n\n\npositive\n19563\n\n\nbs\nnegative\n11974\n\n\nneutral\n11145\n\n\npositive\n13064\n\n\ncs\nnegative\n39674\n\n\nneutral\n59200\n\n\npositive\n97413\n\n\nde\nnegative\n104667\n\n\nneutral\n100071\n\n\npositive\n111149\n\n\nen\nnegative\n304939\n\n\nneutral\n290823\n\n\npositive\n1734724\n\n\nes\nnegative\n108733\n\n\nneutral\n122493\n\n\npositive\n187486\n\n\nfa\nnegative\n1602\n\n\nneutral\n5091\n\n\npositive\n6832\n\n\nfr\nnegative\n84187\n\n\nneutral\n43245\n\n\npositive\n83199\n\n\nhe\nnegative\n2279\n\n\nneutral\n243\n\n\npositive\n6097\n\n\nhi\nnegative\n4992\n\n\nneutral\n6392\n\n\npositive\n5615\n\n\nhr\nnegative\n19757\n\n\nneutral\n19470\n\n\npositive\n38367\n\n\nhu\nnegative\n8974\n\n\nneutral\n17621\n\n\npositive\n30087\n\n\nit\nnegative\n4043\n\n\nneutral\n4193\n\n\npositive\n3829\n\n\nja\nnegative\n83982\n\n\nneutral\n41979\n\n\npositive\n83819\n\n\nlv\nnegative\n1378\n\n\nneutral\n2618\n\n\npositive\n1794\n\n\npl\nnegative\n77422\n\n\nneutral\n62074\n\n\npositive\n97192\n\n\npt\nnegative\n56827\n\n\nneutral\n55165\n\n\npositive\n45842\n\n\nru\nnegative\n31770\n\n\nneutral\n48106\n\n\npositive\n31054\n\n\nsk\nnegative\n14431\n\n\nneutral\n12842\n\n\npositive\n29350\n\n\nsl\nnegative\n33694\n\n\nneutral\n50553\n\n\npositive\n29296\n\n\nsq\nnegative\n6889\n\n\nneutral\n14757\n\n\npositive\n22638\n\n\nsr\nnegative\n25089\n\n\nneutral\n32283\n\n\npositive\n18996\n\n\nsv\nnegative\n16266\n\n\nneutral\n13342\n\n\npositive\n11738\n\n\nth\nnegative\n9326\n\n\nneutral\n28616\n\n\npositive\n34377\n\n\nur\nnegative\n5239\n\n\nneutral\n8585\n\n\npositive\n5836\n\n\nzh\nnegative\n117967\n\n\nneutral\n69016\n\n\npositive\n144719\n\n\n\n\n\n\n\n\n\nTexts in Language Family and Genus\n\npd.DataFrame(mms_dataset_df.groupby(by=['Family', 'Genus',]).count()[\"text\"])\n\n\n\n\n\n\n\n\n\ntext\n\n\nFamily\nGenus\n\n\n\n\n\nAfro-Asiatic\nSemitic\n940694\n\n\nIndo-European\nAlbanian\n44284\n\n\nBaltic\n5790\n\n\nGermanic\n2687719\n\n\nIndic\n36659\n\n\nIranian\n13525\n\n\nRomance\n799242\n\n\nSlavic\n966366\n\n\nJapanese\nJapanese\n209780\n\n\nSino-Tibetan\nChinese\n331702\n\n\nTai-Kadai\nKam-Tai\n72319\n\n\nUralic\nUgric\n56682\n\n\n\n\n\n\n\n\n\nExamples per domain\n\npd.DataFrame(mms_dataset_df.groupby(by=[\"domain\"]).count()[\"text\"])\n\n\n\n\n\n\n\n\ntext\n\n\ndomain\n\n\n\n\n\nchats\n16781\n\n\nmixed\n94122\n\n\nnews\n26413\n\n\npoems\n1052\n\n\nreviews\n4510893\n\n\nsocial_media\n1515501" + }, + { + "objectID": "dataset_card.html#hosting-licensing-and-maintenance-plan", + "href": "dataset_card.html#hosting-licensing-and-maintenance-plan", + "title": "MMS Dataset Card", + "section": "Hosting, Licensing, and Maintenance Plan", + "text": "Hosting, Licensing, and Maintenance Plan\n\nHosting: The datasets and benchmark will be hosted on a reliable and scalable cloud infrastructure to ensure accessibility and availability (HuggingFace Hub). The choice of hosting platform will be based on factors such as reliability, performance, and cost-effectiveness.\nLicensing: We will clearly state the data license under which the datasets are released, ensuring that the terms of use are explicitly defined. We will consider licenses that facilitate research and allow for derivative works, while also addressing potential ethical considerations. See the license in repository.\nMaintenance: We (see Dataset Curators section) are committed to providing ongoing maintenance and support for the datasets and benchmark. This includes regular updates, bug fixes, and addressing any user feedback or inquiries. We will also establish a communication channel for users to report issues or request assistance." + } +] \ No newline at end of file diff --git a/site_libs/bootstrap/bootstrap-icons.css b/site_libs/bootstrap/bootstrap-icons.css new file mode 100644 index 0000000..94f1940 --- /dev/null +++ b/site_libs/bootstrap/bootstrap-icons.css @@ -0,0 +1,2018 @@ +@font-face { + font-display: block; + font-family: "bootstrap-icons"; + src: +url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff"); +} + +.bi::before, +[class^="bi-"]::before, +[class*=" bi-"]::before { + display: inline-block; + font-family: bootstrap-icons !important; + font-style: normal; + font-weight: normal !important; + font-variant: normal; + text-transform: none; + line-height: 1; + vertical-align: -.125em; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +.bi-123::before { content: "\f67f"; } +.bi-alarm-fill::before { content: "\f101"; } +.bi-alarm::before { content: 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this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new 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b/site_libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > * " + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + console.log({ + childtop: marginChild.getBoundingClientRect().top, + scroll: window.scrollY, + top, + lastBottom, + }); + if (top < lastBottom) { + const margin = lastBottom - top; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + + console.log({ + top, + height: marginChild.getBoundingClientRect().height, + marginTop, + total: top + marginChild.getBoundingClientRect().height + marginTop, + }); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(layoutMarginEls, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of Object.entries(getTabSettings())) { + const selectors = selectorsToSync[group]; + // it's possible that stale state gives us empty selections, so we explicitly check here. + if (selectors) { + toggleAll(selectedName, selectors); + } + } +}); + +function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; +} + +function nexttick(func) { + return setTimeout(func, 0); +} diff --git a/site_libs/quarto-html/tippy.css b/site_libs/quarto-html/tippy.css new file mode 100644 index 0000000..e6ae635 --- /dev/null +++ b/site_libs/quarto-html/tippy.css @@ -0,0 +1 @@ +.tippy-box[data-animation=fade][data-state=hidden]{opacity:0}[data-tippy-root]{max-width:calc(100vw - 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b/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,277 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver( + updateDocumentOffsetWithoutAnimation + ); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/site_libs/quarto-search/autocomplete.umd.js b/site_libs/quarto-search/autocomplete.umd.js new file mode 100644 index 0000000..619c57c --- /dev/null +++ b/site_libs/quarto-search/autocomplete.umd.js @@ -0,0 +1,3 @@ +/*! @algolia/autocomplete-js 1.7.3 | MIT License | © Algolia, Inc. and contributors | https://github.com/algolia/autocomplete */ +!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?t(exports):"function"==typeof define&&define.amd?define(["exports"],t):t((e="undefined"!=typeof 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Apache Software License 2.0 + * + * http://www.apache.org/licenses/LICENSE-2.0 + */ +var e,t;e=this,t=function(){"use strict";function e(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function t(t){for(var n=1;ne.length)&&(t=e.length);for(var n=0,r=new Array(t);n0&&void 0!==arguments[0]?arguments[0]:1,t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:3,n=new Map,r=Math.pow(10,t);return{get:function(t){var i=t.match(C).length;if(n.has(i))return n.get(i);var o=1/Math.pow(i,.5*e),c=parseFloat(Math.round(o*r)/r);return n.set(i,c),c},clear:function(){n.clear()}}}var $=function(){function e(){var t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},n=t.getFn,i=void 0===n?I.getFn:n,o=t.fieldNormWeight,c=void 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a/site_libs/quarto-search/quarto-search.js b/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..f5d852d --- /dev/null +++ b/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1140 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query !== null && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query !== null && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = () => { + setIsOpen(false); + }; + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.map((event) => { + const maxEvents = event.objectIDs.slice(0, 20); + return { + ...event, + objectIDs: maxEvents, + }; + }); + + insights.viewedObjectIDs(...events); + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + // create fuse index + const options = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, + }; + const fuse = new window.Fuse([], options); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard(createElement, icon, title, section, text, href) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + [iconEl, titleEl] + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text"].forEach((keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + }); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +function fuseSearch(query, fuse, fuseOptions) { + return fuse.search(query, fuseOptions).map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + }; + }); +} diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 0000000..877073d --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,23 @@ + + + + https://Brand24-AI.github.io/mms_benchmark/mms_benchmark/training_example.html + 2023-11-14T11:02:35.420Z + + + https://Brand24-AI.github.io/mms_benchmark/mms_benchmark/citations.html + 2023-11-14T11:02:31.748Z + + + https://Brand24-AI.github.io/mms_benchmark/mms_benchmark/index.html + 2023-11-14T11:02:27.312Z + + + https://Brand24-AI.github.io/mms_benchmark/mms_benchmark/benchmark_results.html + 2023-11-14T11:02:28.408Z + + + https://Brand24-AI.github.io/mms_benchmark/mms_benchmark/dataset_card.html + 2023-11-14T11:02:35.108Z + + diff --git a/styles.css b/styles.css new file mode 100644 index 0000000..66ccc49 --- /dev/null +++ b/styles.css @@ -0,0 +1,37 @@ +.cell { + margin-bottom: 1rem; +} + +.cell > .sourceCode { + margin-bottom: 0; +} + +.cell-output > pre { + margin-bottom: 0; +} + +.cell-output > pre, .cell-output > .sourceCode > pre, .cell-output-stdout > pre { + margin-left: 0.8rem; + margin-top: 0; + background: none; + border-left: 2px solid lightsalmon; + border-top-left-radius: 0; + border-top-right-radius: 0; +} + +.cell-output > .sourceCode { + border: none; +} + +.cell-output > .sourceCode { + background: none; + margin-top: 0; +} + +div.description { + padding-left: 2px; + padding-top: 5px; + font-style: italic; + font-size: 135%; + opacity: 70%; +} diff --git a/training_example.html b/training_example.html new file mode 100644 index 0000000..b916b7e --- /dev/null +++ b/training_example.html @@ -0,0 +1,600 @@ + + + + + + + + + +mms_benchmark - MMS - Example training pipeline + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

MMS - Example training pipeline

+
+ + + +
+ + + + +
+ + +
+ + +
+
!pip install datasets transformers==4.30.0 torch sacremoses scikit-learn evaluate accelerate
+
+
+
import os
+
+import evaluate
+import numpy as np
+from datasets import load_dataset
+from transformers import (
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    Trainer,
+    TrainingArguments,
+)
+
+

Our dataset is publicly available but we need to you to accept conditions. Please see this link, accept the terms

+
+
mms_dataset = load_dataset("Brand24/mms")
+
+
Downloading and preparing dataset mms/default to /root/.cache/huggingface/datasets/Brand24___mms/default/0.2.0/70532fdd01f149ff84a280b7d9cfb661643abf4837b4f0f3aa1128064e870d65...
+Dataset mms downloaded and prepared to /root/.cache/huggingface/datasets/Brand24___mms/default/0.2.0/70532fdd01f149ff84a280b7d9cfb661643abf4837b4f0f3aa1128064e870d65. Subsequent calls will reuse this data.
+
+
+ +
+
+ +
+
+ +
+
+ +
+
+

There are 14 different dimensions which differentiate obtained datasets. In addition, there is a pre-calculated cleanlab self conficence score for each sample. All of them can be used to sample examples which suit our use case best

+
+
mms_dataset.column_names
+
+
{'train': ['_id',
+  'text',
+  'label',
+  'original_dataset',
+  'domain',
+  'language',
+  'Family',
+  'Genus',
+  'Definite articles',
+  'Indefinite articles',
+  'Number of cases',
+  'Order of subject, object, verb',
+  'Negative morphemes',
+  'Polar questions',
+  'Position of negative word wrt SOV',
+  'Prefixing vs suffixing',
+  'Coding of nominal plurality',
+  'Grammatical genders',
+  'cleanlab_self_confidence']}
+
+
+

Select only samples in polish and coming from social media

+
+
pl_sm = mms_dataset["train"].filter(lambda x: x["language"] == "pl" and x["domain"] == "social_media")
+
+ +
+
+

To achieve higher performance, we will select only samples with high self confidence score

+
+
pl_sm_high_confidence = pl_sm.filter(lambda x: x["cleanlab_self_confidence"] > 0.6)
+
+ +
+
+
+
len(pl_sm_high_confidence)
+
+
73227
+
+
+

We will use this examples to fine-tune Polish version of BERT model - HerBERT

+
+
tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-base-cased")
+
+def tokenize(batch):
+    return tokenizer(batch["text"], padding="max_length", truncation=True)
+
+
+
tokenized_dataset = pl_sm_high_confidence.map(tokenize, batched=True, batch_size=512)
+
+ +
+
+
+
model = AutoModelForSequenceClassification.from_pretrained("allegro/herbert-base-cased", num_labels=3)
+
+
Some weights of the model checkpoint at allegro/herbert-base-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.sso.sso_relationship.weight', 'cls.predictions.decoder.bias', 'cls.sso.sso_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias']
+- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
+- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
+Some weights of BertForSequenceClassification were not initialized from the model checkpoint at allegro/herbert-base-cased and are newly initialized: ['classifier.weight', 'classifier.bias']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+
+
+
+
split_dataset = tokenized_dataset.train_test_split(test_size=0.1)
+train_dataset = split_dataset["train"]
+eval_dataset = split_dataset["test"]
+
+
+
training_args = TrainingArguments(
+    output_dir="PL_SM_SENT",
+    evaluation_strategy="epoch",
+    num_train_epochs=1,
+)
+metric = evaluate.load("accuracy")
+
+
+def compute_metrics(eval_pred):
+    logits, labels = eval_pred
+    predictions = np.argmax(logits, axis=-1)
+    return metric.compute(predictions=predictions, references=labels)
+
+
+
trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    compute_metrics=compute_metrics,
+)
+
+
+
trainer.train()
+
+
/opt/conda/lib/python3.10/site-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
+  warnings.warn(
+
+
+ + + +
+ + +
+ + + + \ No newline at end of file