From Dictionaries to LLMs: Text Analysis in R  

This workshop walks through a complete text analysis pipeline in R, combining traditional methods with newer AI-powered tools.

In the first part, we will cover tokenization with tidytext, stopword removal (including custom stopwords), word frequency visualization with wordclouds and bar plots, dictionary-based sentiment analysis using three lexicons (AFINN, Bing, and NRC), topic modeling with LDA, and bigram analysis with word networks.

In the second part, we explore how the mall package can be used to perform sentiment analysis with local LLMs (via Ollama), comparing this approach to dictionary-based methods, and look at related tasks like text classification and entity extraction.

Familiarity with R and the tidyverse is assumed.