This looks really interesting, but it pretty hard to follow without the video.

My summary after a quick flick through is that it is a better classification(/clustering?) model for text, because it takes Word2Vec-style similarity into account, which plain LDA doesn't. That sounds like a reasonable approach to me, and nice to see someone get it working.

I think. Comments?

Here is the version with notes. I haven't read this through yet: http://www.slideshare.net/ChristopherMoody3/word2vec-lda-and...

Code here, BTW: https://github.com/cemoody/lda2vec

Previous work by Chris on word2vec

http://multithreaded.stitchfix.com/blog/2015/03/11/word-is-w...

I know word2vec and LDA separately, but what does this work do? Somehow combine the word similarities from word2vec when forming LDA topics?

Word2Vec is based on an original approach from Lawrence Berkeley National Lab. This was also at the same time that David Blei was working on LDA at Berkeley. https://www.kaggle.com/c/word2vec-nlp-tutorial/forums/t/1234...

what does v_client mean in the page 108 of the slides?

awesome work :)