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