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Activity Number: 79
Type: Contributed
Date/Time: Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Graphics
Abstract #312233
Title: An Interactive Visualization Platform for Interpreting Topic Models
Author(s): Carson Sievert*+ and Kenny Shirley
Companies: Iowa State University and AT&T Labs
Keywords: Latent Dirichlet Allocation ; Topic Model ; Bayesian Statistics ; Information Visualization ; Dynamic ; Interactive

A popular approach to understanding large amounts of textual information is topic modeling. In a topic model, it is assumed that each "document" is derived from a possibly different mixture of latent topics where each topic has its own probability mass function over a set vocabulary. Interpreting topics can often be difficult since each topic has a large multinomial distribution of potentially thousands of words. I will present a general framework for visualizing topic models that utilizes interaction to interpret and compare topics by highlighting keywords.

Authors who are presenting talks have a * after their name.

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