ALTES

Interpreting the content of a large number of stored documents is challenging. Topic modeling is an unsupervised machine learning technique that supports this interpretation by identifying groups of words related to the same subject into sets of documents. However, interpreting the generated topics can be complex due to the lack of a straightforward semantic context in the grouped words. To address this challenge, the paper presents the ALTES labeling tool, which supports the interpretation of topics generated by the topic modeling technique through enrichment with data from external sources. ALTES finds words related to the terms that compose the topics and establishes associations between ideas or concepts that are not initially evident in the identified topics.