dselivanov/text2vec

Topic modeling guide

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#262 opened on May 26, 2018

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help wanted

Description

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?

Contributor guide

Tech stack
None
Domain
documentation
Issue type
documentation
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
3-5 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
clear
Prerequisites
Rtext2vec packagetopic modeling basics
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
40
Research direction
Create a comprehensive guide for topic modeling using text2vec and udpipe. Follow the steps outlined in the issue: find a nontrivial corpus, tokenize with POS tags, build collocations, create DTM, fit LDA and WRMF models, and evaluate with coherence metrics. Use existing vignettes from udpipe as inspiration. Check comments for any additional input from maintainers.