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If I want to get how many and what kind of topics are covered by New York Times each week from a bag of words model(All the news covered by NYT in a week) how should I approach? Using traditional unsupervised LDA didn't help much.

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  • $\begingroup$ You may want to check out Giveme5W1H, which is an event extractor for news articles, extracting the 5W and 1H journalistic questions, i.e., who did what, when, where, why, and how? $\endgroup$
    – pedjjj
    Commented Dec 18, 2019 at 15:41

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I'm not an expert in this field but you should take a look at the work of Bhargav Srinivasa Desikan, a gensim contributor, who works a lot with topic modelling.

He has a couple of notebooks on his github account which could interest you, especially this one (should be pretty much your use case if I understand your problem correctly).

The aforementioned notebook evaluates Latent Semantic Indexing, the Hierarchical Dirichlet Process as well as the Latent Dirichlet Allocation for identifying topics.

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