R-Ladies Freiburg (English) - Text analysis with R: Topic modeling
Channel: R-Ladies Global
Date: 22/04/2022

Taught by Julia Müller (@JuliaMuellerFr), PhD student at the University of Freiburg.

Topic modeling classifies a collection of documents (those can be articles, books, song lyrics, social media posts...) into natural groups, which we can then analyse to help us better understand the text. This is an unsupervised classification, similar to clustering for numeric data. It operates on the principle that every document is a mixture of different topics, and every topic is a micture of words.
In this workshop, we first demonstrated the basics of topic modeling with newspaper articles. Then, we put the method to the test by mixing up chapters from three different books and checking if topic modeling can correctly sort the chapters back into the books they appear in. Finally, we finished up with an exercise on clustering song lyrics by Beyoncé, and Taylor Swift.

The workshop was based on Chapter 6 in Tidy Text Mining (Julia Silge & David Robinson): https://www.tidytextmining.com/topicmodeling.html

All materials are available on the R-Ladies Freiburg Github repo: 
https://github.com/rladies/meetup-presentations_freiburg

This event was originally live on Zoom with R-Ladies Freiburg. Check out our upcoming events here: https://www.meetup.com/rladies-freiburg