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?