GitHub - ryanskeller48/LSATopicModeling: Topic Modeler based on Latent Semantic Analysis. Used to identify commonalities and differences within a corpus of documents. Identifies the top 5% of keywords for each topic that appears in the set of documents and identifies the documents with the strongest association to each topic. Our corpus consists of sell-side reports on many companies prepared by different analysts -- we wanted to see what keywords and topics were common among these analyses. In theory, this could help tease out bias in competing analyses. While our analysis focuses on financial documents, any corpus can theoretically be used and common topics found.

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Topic Modeler based on Latent Semantic Analysis. Used to identify commonalities and differences within a corpus of documents. Identifies the top 5% of keywords for each topic that appears in the set of documents and identifies the documents with the strongest association to each topic. Our corpus consists of sell-side reports on many companies p…