I have used NMF and LDA for topic modelling in Python, with what I would call good results with NMF, and poor results with LDA.

My data is highly domain-specific, with a lot of unique/specific vocabulary.

Also, each of my documents/observations is short, ranging from 10-50 words.

I am trying to improve my NMF output by trying some other methods.

Can anyone recommend what I should research/learn yet?


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