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This seminar will discuss one of the most popular techniques for automatically identifying latent or hidden themes in text: topic models. We will begin with a brief, high-level introduction to Latent Dirichlet Allocation but spent most of the hour discussing how to write code to perform topic modeling on a corpus of political statements. This webinar will cover both conventional LDA as well as Structural Topic Modeling— a more recent technique that employs meta-data to improve classification of documents according to latent themes or topics. This course assumes a basic working knowledge of R, and the content covered in an earlier “Introduction to Text Analysis” webinar that covers text preprocessing and creating document-term matrices.

On the day before the session, all registrants will receive an e-mail with a link and meeting information. Please register at least 15 minutes prior to the start of the session to allow time for the system to generate a personalized e-mail with the access link.

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Status	Archived
Date	Wednesday, March 31st, 2021
Time	12:30pm - 1:30pm
Location	Virtual Classroom
Leader	Chris Bail
Enrolled	33