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

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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.
Publisher
IEEE COMPUTER SOC
Issue Date
2013-12
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, v.25, no.12, pp.2740 - 2751

ISSN
1041-4347
DOI
10.1109/TKDE.2012.238
URI
http://hdl.handle.net/10203/254430
Appears in Collection
CS-Journal Papers(저널논문)
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