Disputant Relation-Based Classification for Contrasting Opposing Views of Contentious News Issues

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dc.contributor.authorPark, Souneilko
dc.contributor.authorKim, Jungilko
dc.contributor.authorLee, Kyung Soonko
dc.contributor.authorSong, Junehwako
dc.date.accessioned2019-04-15T14:50:50Z-
dc.date.available2019-04-15T14:50:50Z-
dc.date.created2013-02-01-
dc.date.issued2013-12-
dc.identifier.citationIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, v.25, no.12, pp.2740 - 2751-
dc.identifier.issn1041-4347-
dc.identifier.urihttp://hdl.handle.net/10203/254430-
dc.description.abstractContentious news issues, such as the health care reform debate, draw much interest from the public; however, it is not simple for an ordinary user to search and contrast the opposing arguments and have a comprehensive understanding of the issues. Providing a classified view of the opposing views of the issues can help readers easily understand the issue from multiple perspectives. We present a disputant relation-based method for classifying news articles on contentious issues. We observe that the disputants of a contention are an important feature for understanding the discourse. It performs unsupervised classification on news articles based on disputant relations, and helps readers intuitively view the articles through the opponent-based frame and attain balanced understanding, free from a specific biased viewpoint. The method is performed in three stages: disputant extraction, disputant partitioning, and article classification. We apply a modified version of HITS algorithm and an SVM classifier trained with pseudorelevant data for article analysis. We conduct an accuracy analysis and an upper-bound analysis for the evaluation of the method.-
dc.languageEnglish-
dc.publisherIEEE COMPUTER SOC-
dc.titleDisputant Relation-Based Classification for Contrasting Opposing Views of Contentious News Issues-
dc.typeArticle-
dc.identifier.wosid000326500600006-
dc.identifier.scopusid2-s2.0-84887863540-
dc.type.rimsART-
dc.citation.volume25-
dc.citation.issue12-
dc.citation.beginningpage2740-
dc.citation.endingpage2751-
dc.citation.publicationnameIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING-
dc.identifier.doi10.1109/TKDE.2012.238-
dc.contributor.localauthorSong, Junehwa-
dc.contributor.nonIdAuthorPark, Souneil-
dc.contributor.nonIdAuthorKim, Jungil-
dc.contributor.nonIdAuthorLee, Kyung Soon-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorHuman information processing-
dc.subject.keywordAuthorclustering-
dc.subject.keywordAuthorclassification-
dc.subject.keywordAuthorand association rules-
dc.subject.keywordAuthortext mining-
dc.subject.keywordAuthorinformation browsers-
dc.subject.keywordAuthordocument analysis-
dc.subject.keywordAuthorlibraries/information repositories/publishing-
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