This is the second event in the Data-Science forum Tech Up at GU. Come listen to Gard Olav Dietrichson!

Using R and Py to Classify Debates: A Supervised and Unsupervised Approach

Oct 17 2026 Gard Olav Dietrichson:

What You’ll Learn: Structural Topic Modeling in R

This session introduces how to uncover hidden themes in large text collections using Structural Topic Modeling (STM). Participants will learn to:

Prepare and clean text data for analysis.

Tokenize and filter words, removing stopwords and applying word stemming.

Use TF-IDF to identify meaningful words and visualize word frequency patterns.

Build and interpret an STM, discovering key topics and how they vary across groups (e.g., by gender).

Load and prepare labeled text data, cleaning and restructuring it for analysis.

Convert model predictions into binary classifications for comparison.

Join automated and manual classifications to assess accuracy.

Use confusion matrices to measure model performance across different topics.

• Repository