Topic modeling guide

It will be useful to create a comprehensive practical guide for topic modeling. Now we have all components in place:

• POS tags and lemmatization - thanks to udpipe package
• coherence measures - thanks to Manuel work
• fast LDA, thanks to WarpLDA in text2vec
• fast non-negative matrix factorization, thanks to rsparse package
• multi-word phrase extraction - several approaches text2vec::Collocations, udpipe::as_phrasemachine

Steps

• find interesting non-trivial corpus with large number of documents
• demonstrate how to create tokenizer which only use particular POS
• create collocation model on top of that
• create document-term matrix using tokens with multi-word expression
• fit several topic models (text2vec::LDA, rsparse::WRMF) with different hyper parameters
• cross-validate / compare them using different coherence metrics
  • demonstrate usage of external corpus for tcm calculation
  • check on how coherence metrics are correlated (is perplexity correlated with them? )

There are already good vignettes in udpipe package topic modeling and phrase extraction. They can be used as inspiration.

@manuelbickel @jwijffels anything we can add to the plan above?