Though primarily introduced to find latent topics in text documents, topic models have proven to be relevant in a wide range of contexts. read more
Use cases are endless, one just has to think about LDA as a generative process (be careful though, as you might start to see topic models everywhere). On top of my head, I can think of: Massive automatic movies indexation from subtitles. read more
Then there are a whole family of techniques related to LDA — Topics Over Time, Dynamic Topic Modeling, Hierarchical LDA, Pachinko Allocation — that one can explore rapidly enough by searching the web. In general, it’s a good idea to approach these skeptically. read more
13. View the topics in LDA model. The above LDA model is built with 20 different topics where each topic is a combination of keywords and each keyword contributes a certain weightage to the topic. You can see the keywords for each topic and the weightage(importance) of each keyword using lda_model.print_topics() as shown next. read more