Director of Research (if dissertation) or Advisor (if thesis)
Zhai, ChengXiang
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Text mining
Topic models
Time series
Abstract
Events in the world generate an enormous amount of textual data like tweets and news articles. These events also manifest in the form of changes to time-series numeric data. This thesis deals with the problem of extracting these events from the timestamped document collection in the form of topics that cause a change in a time-series. We develop a conceptual framework for that can be used to analyze different causal topic mining algorithms. We also propose two novel clustering based algorithms - cCTM-CF and cCTM-CoF to generate causal topics. We evaluate these algorithms both qualitatively, and quantitatively by comparing their coherence and correlation scores to that of the baseline generative causal topic model - gCTM. We found that cCTM-CoF performs 35% and 62.5% better according to these metrics as compared to the baseline.
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