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](https://cran.r-project.org/web/packages/udpipe/vignettes/udpipe-usecase-topicmodelling.html) and [phrase extraction](https://cran.r-project.org/web/packages/udpipe/vignettes/udpipe-usecase-postagging-lemmatisation.html). They can be used as inspiration.

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