{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90948"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90948","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Time series analysis for event detection in text data","abstract":"With the rise of social media and online newswire, text streams are attracting more and more research interest. These streams are presented in the form of time series by nature, therefore, how to efficiently analyze these time series and extract useful information from them are of great importance. Modern time series analysis (TSA) has been applied widely in areas such as finance, physics and signal processing, however, there is not so much working exploring time series analysis in the field of text mining. While traditional time series analysis tasks are relatively well defined such as modeling and forecasting, we now need to adapt the tasks to meet the requirement of different text mining problems. Event detection is the general task of finding any emerging events, such as significant changes in stock price, anomalies in climate data, and outbreaks of a certain disease, depending on the data we are interested in. While in text mining, event detection, which is identifying the significant new stories, is attracting more research attention given the increasing popularity of social media and digital journalism. In time series analysis, there is also a common task, change point detection, which focuses on a similar challenge. In this thesis work, we first examine the features presented by the time series of counts of terms in corpus. We then explore applying existing change point detection methods to event detection, and also propose a novel TSA based method for event detection.","abstract_html":"With the rise of social media and online newswire, text streams are attracting more and more research interest. These streams are presented in the form of time series by nature, therefore, how to efficiently analyze these time series and extract useful information from them are of great importance. Modern time series analysis (TSA) has been applied widely in areas such as finance, physics and signal processing, however, there is not so much working exploring time series analysis in the field of text mining. While traditional time series analysis tasks are relatively well defined such as modeling and forecasting, we now need to adapt the tasks to meet the requirement of different text mining problems. Event detection is the general task of finding any emerging events, such as significant changes in stock price, anomalies in climate data, and outbreaks of a certain disease, depending on the data we are interested in. While in text mining, event detection, which is identifying the significant new stories, is attracting more research attention given the increasing popularity of social media and digital journalism. In time series analysis, there is also a common task, change point detection, which focuses on a similar challenge. In this thesis work, we first examine the features presented by the time series of counts of terms in corpus. We then explore applying existing change point detection methods to event detection, and also propose a novel TSA based method for event detection.","abstract_has_math":false,"creators":["Zhu, Rongda"],"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":2016,"date_issued":"2016-07-07T21:18:00Z","date_published":"2016-07-07T21:18:00Z","updated_at":"2026-07-22T22:26:34Z","subjects":["Event detection"],"languages":["en"],"rights":["Copyright 2016 Rongda Zhu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/90948","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":["Zhu, Rongda"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-07-07T21:18:00Z","2018-07-08T09:15:23Z","2016-04-25","2016-05"]},{"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":["Event detection"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Rongda Zhu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/90948"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["With the rise of social media and online newswire, text streams are attracting more and more research interest. These streams are presented in the form of time series by nature, therefore, how to efficiently analyze these time series and extract useful information from them are of great importance. Modern time series analysis (TSA) has been applied widely in areas such as finance, physics and signal processing, however, there is not so much working exploring time series analysis in the field of text mining. While traditional time series analysis tasks are relatively well defined such as modeling and forecasting, we now need to adapt the tasks to meet the requirement of different text mining problems. Event detection is the general task of finding any emerging events, such as significant changes in stock price, anomalies in climate data, and outbreaks of a certain disease, depending on the data we are interested in. While in text mining, event detection, which is identifying the significant new stories, is attracting more research attention given the increasing popularity of social media and digital journalism. In time series analysis, there is also a common task, change point detection, which focuses on a similar challenge. In this thesis work, we first examine the features presented by the time series of counts of terms in corpus. We then explore applying existing change point detection methods to event detection, and also propose a novel TSA based method for event detection.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-05-01","The student, Rongda Zhu, accepted the attached license on 2016-04-22 at 12:28.","The student, Rongda Zhu, submitted this Thesis for approval on 2016-04-22 at 12:35.","This Thesis was approved for publication on 2016-04-25 at 17:02.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9438 on 2016-07-07 at 14:17:55","Made available in DSpace on 2016-07-07T21:18:00Z (GMT). No. of bitstreams: 2 ZHU-THESIS-2016.pdf: 301671 bytes, checksum: f6fd8dbcaf277b3b41281b1de725c32f (MD5) LICENSE.txt: 4207 bytes, checksum: f4b59da4ff32a0bbc057851f7c0360ee (MD5) Previous issue date: 2016-04-25","Embargo set by: Seth Robbins for item 93303 Lift date: 2018-07-07T21:18:16Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 93303 on 2018-07-08T09:15:23Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Time series analysis for event detection in text data"]}]}],"canonical_facts":{"dc:contributor":["Zhai, ChengXiang"],"dc:creator":["Zhu, Rongda"],"dc:date":["2016-07-07T21:18:00Z","2018-07-08T09:15:23Z","2016-04-25","2016-05"],"dc:description":["With the rise of social media and online newswire, text streams are attracting more and more research interest. These streams are presented in the form of time series by nature, therefore, how to efficiently analyze these time series and extract useful information from them are of great importance. Modern time series analysis (TSA) has been applied widely in areas such as finance, physics and signal processing, however, there is not so much working exploring time series analysis in the field of text mining. While traditional time series analysis tasks are relatively well defined such as modeling and forecasting, we now need to adapt the tasks to meet the requirement of different text mining problems. Event detection is the general task of finding any emerging events, such as significant changes in stock price, anomalies in climate data, and outbreaks of a certain disease, depending on the data we are interested in. While in text mining, event detection, which is identifying the significant new stories, is attracting more research attention given the increasing popularity of social media and digital journalism. In time series analysis, there is also a common task, change point detection, which focuses on a similar challenge. In this thesis work, we first examine the features presented by the time series of counts of terms in corpus. We then explore applying existing change point detection methods to event detection, and also propose a novel TSA based method for event detection.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-05-01","The student, Rongda Zhu, accepted the attached license on 2016-04-22 at 12:28.","The student, Rongda Zhu, submitted this Thesis for approval on 2016-04-22 at 12:35.","This Thesis was approved for publication on 2016-04-25 at 17:02.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9438 on 2016-07-07 at 14:17:55","Made available in DSpace on 2016-07-07T21:18:00Z (GMT). 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