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Activity Number: 47 - Highlights from Bayesian Analysis
Type: Invited
Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #300151 Presentation
Title: Bayesian Analysis of Dynamic Linear Topic Models
Author(s): Christopher Glynn* and Surya Tokdar and David Banks and Brian Howard
Companies: University of New Hampshire and Duke University and SAMSI/Duke University and Sciome, LLC
Keywords: Topic Models; Dynamic Linear Models; Polya-Gamma; MCMC

Discovering temporal evolution of themes from a time-stamped collection of text poses a challenging statistical learning problem. Dynamic topic models offer a probabilistic modeling framework to decompose a corpus of text documents into “topics”, i.e., probability distributions over vocabulary terms, while simultaneously learning the temporal dynamics of the relative prevalence of these topics. We extend the dynamic topic model of Blei and Lafferty (2006) by fusing its multinomial factor model on topics with dynamic linear models that account for time trends and seasonality in topic prevalence. A Markov chain Monte Carlo (MCMC) algorithm that utilizes Pólya-Gamma data augmentation is developed for posterior sampling. Our model and inference algorithm are validated with multiple synthetic examples, and we consider the applied problem of modeling trends in real estate listings from the housing website Zillow. Analysis of the Zillow corpus demonstrates that the method is able to learn seasonal patterns and locally linear trends in topic prevalence.

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

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