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Title:Clustering based causal topic mining
Author(s):Mohan, Vishaal
Advisor(s):Zhai, ChengXiang
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(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.
Issue Date:2017-04-25
Type:Thesis
URI:http://hdl.handle.net/2142/97626
Rights Information:Copyright 2017 Vishaal Mohan
Date Available in IDEALS:2017-08-10
Date Deposited:2017-05


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