I am having a little friendly debate with my coworker on how to properly/optimally do topic modeling. I am just using the regular traditional nmf/lda approach and he decided to do it using "skip grams" in the sense that he just took every single bigram word combination in every document within N words (he did it within 5) and labels it as a topic, sorts it for those that appear most commonly and filters out non sensical topics.
Can you please tell me if there is any validity to this approach over LDA/NMF?
To me it seems like this is just equivalent to running regexes within 5 words on every possible bigram and not modeling, so there would be no reason to invent LDA/NMF if this "skiagram" approach can possibly to better. But I could be wrong.