- We build a model that builds both word and document topics, makes them interpreable, makes topics over clients, times, and documents, and makes them supervised topics.
- lda2vec also yields topics over clients.
- lda2vec the topics can be ‘supervised’ and forced to predict another target.
- It’s research software, and we’ve tried to make it simple to modify lda2vec and to play around with your own custom topic models.
- LDA on the other hand is quite interpretable by humans, but doesn’t model local word relationships like word2vec.
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@andradeandrey: “Very interesting code, lda2vec tools for interpreting natural language #machinelearning #NLP”
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