A tale about LDA2vec: when LDA meets word2vec

A few days ago I found out that there had appeared lda2vec (by Chris Moody) – a hybrid algorithm combining best ideas from well-known LDA (Latent Dirichlet Allocation) topic modeling algorithm and from a bit less well-known tool for language modeling named word2vec. And now I'm going to tell you a tale about lda2vec and my attempts to try it and compare with simple LDA implementation (I used gensim package for this). What is cool about it? It means that LDA is able to create document (and topic) representations that are not so flexible but mostly interpretable to humans. Also, LDA treats a set of documents as a set of documents, whereas word2vec works with a set of documents as with a very long text string.