{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/97626"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/97626","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Clustering based causal topic mining","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Mohan, Vishaal"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Zhai, ChengXiang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-08-10T19:52:22Z","date_published":"2017-08-10T19:52:22Z","updated_at":"2026-07-22T22:24:34Z","subjects":["Text mining","Topic models","Time series"],"languages":["en"],"rights":["Copyright 2017 Vishaal Mohan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/97626","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhai, ChengXiang"]},{"key":"dc:creator","label":"Author","values":["Mohan, Vishaal"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-08-10T19:52:22Z","2019-08-11T09:15:39Z","2017-04-25","2017-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Text mining","Topic models","Time series"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Vishaal Mohan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/97626"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Clustering based causal topic mining"]}]}],"canonical_facts":{"dc:contributor":["Zhai, ChengXiang"],"dc:creator":["Mohan, Vishaal"],"dc:date":["2017-08-10T19:52:22Z","2019-08-11T09:15:39Z","2017-04-25","2017-05"],"dc:description":["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). 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