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Libraries Used: Scikit-Learn, NLTK, OS, Numpy, pickle

Instructions:

Directories: Put train and test folders in the same directory as the codes.

  1. Bernoulli Naive Bayes: Uses two files: BernoulliNaiveClassifier.py as the algorithm class and NB_main.py as the main file. Run the latter for results.

  2. NBSVM a novel combination of SVM and Naive Bayes. NBSVMpreprocessing.py is a function file which is used in the NBSVM.py. Run the latter for results.

  3. Using Bing Luis Lexicon Features: Submission_Bing_Luis_sentiment.py implements SVM using the aforementioned lexicon. run for results.

  4. submission_pipeline_features.py is the comparision between two pipelines for two different methods of feature extraction.

  5. Submission_SVM.py is our best and final model producing the results stated in the report.

  6. Submission_VADER.py implements the sentiment analysis function of the NLTK library. run for results.

  7. Submission_Ensemble.py implements the ensemble method by using voting classifier. run for results.