DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Souneil | ko |
dc.contributor.author | Kim, Jungil | ko |
dc.contributor.author | Lee, Kyung Soon | ko |
dc.contributor.author | Song, Junehwa | ko |
dc.date.accessioned | 2019-04-15T14:50:50Z | - |
dc.date.available | 2019-04-15T14:50:50Z | - |
dc.date.created | 2013-02-01 | - |
dc.date.issued | 2013-12 | - |
dc.identifier.citation | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, v.25, no.12, pp.2740 - 2751 | - |
dc.identifier.issn | 1041-4347 | - |
dc.identifier.uri | http://hdl.handle.net/10203/254430 | - |
dc.description.abstract | Contentious 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.language | English | - |
dc.publisher | IEEE COMPUTER SOC | - |
dc.title | Disputant Relation-Based Classification for Contrasting Opposing Views of Contentious News Issues | - |
dc.type | Article | - |
dc.identifier.wosid | 000326500600006 | - |
dc.identifier.scopusid | 2-s2.0-84887863540 | - |
dc.type.rims | ART | - |
dc.citation.volume | 25 | - |
dc.citation.issue | 12 | - |
dc.citation.beginningpage | 2740 | - |
dc.citation.endingpage | 2751 | - |
dc.citation.publicationname | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | - |
dc.identifier.doi | 10.1109/TKDE.2012.238 | - |
dc.contributor.localauthor | Song, Junehwa | - |
dc.contributor.nonIdAuthor | Park, Souneil | - |
dc.contributor.nonIdAuthor | Kim, Jungil | - |
dc.contributor.nonIdAuthor | Lee, Kyung Soon | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Human information processing | - |
dc.subject.keywordAuthor | clustering | - |
dc.subject.keywordAuthor | classification | - |
dc.subject.keywordAuthor | and association rules | - |
dc.subject.keywordAuthor | text mining | - |
dc.subject.keywordAuthor | information browsers | - |
dc.subject.keywordAuthor | document analysis | - |
dc.subject.keywordAuthor | libraries/information repositories/publishing | - |
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