I want to know how the NMF model works in giving out the top topic words from each topic. I mean when I set 50 topics and top words to be returned from each topic to be 4, then is there some weights associated with each of those words in the topics?What I want to do is for a topic I'm getting ngrams like the following:Topic #5:[u'hour block', u'block hour', u'package hour', u'hour block hour']So before this top topic words calculation is done, I want ngrams like hour block block hour to be grouped as one, so that I'm able to get more informative and different top 4 ngrams under each topic.Does this make sense? Is it possible? Thanks!
What goes behind calculating the top topic words for each topic in the Non Negative Matrix Factorization model
python;nlp;topic model;lda