2026-09-19T04:40:28Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/97626 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Zhai, ChengXiang Mohan, Vishaal 2017-08-10T19:52:22Z 2017-08-10T19:52:22Z 2019-08-11T09:15:39Z 2017-04-25 2017-05 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. Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2019-05-01 The student, Vishaal Mohan, accepted the attached license on 2017-04-24 at 14:23. The student, Vishaal Mohan, submitted this Thesis for approval on 2017-04-24 at 14:28. This Thesis was approved for publication on 2017-04-25 at 11:02. DSpace SAF Submission Ingestion Package generated from Vireo submission #11014 on 2017-08-10 at 14:32:32 Made available in DSpace on 2017-08-10T19:52:22Z (GMT). No. of bitstreams: 2 MOHAN-THESIS-2017.pdf: 386969 bytes, checksum: 59c2389baaf5254174bf10112078b1c6 (MD5) LICENSE.txt: 4210 bytes, checksum: 87b6e69d56363c7d9b862972ab545b91 (MD5) Previous issue date: 2017-04-25 Embargo set by: Colleen Fallaw for item 102679 Lift date: 2019-08-10T21:25:30Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system Limited Restriction Lifted for Item 102679 on 2019-08-11T09:15:39Z. application/pdf http://hdl.handle.net/2142/97626 en Copyright 2017 Vishaal Mohan Text mining Topic models Time series Clustering based causal topic mining text text Computer Science Computer Science University of Illinois at Urbana-Champaign Thesis M.S.