Contrasting opposing views of news articles on contentious issues

  • Authors:
  • Souneil Park;KyungSoon Lee;Junehwa Song

  • Affiliations:
  • Korea Advanced Institute of Science and Technology, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea;Chonbuk National University, Jeonbuk, Republic of Korea;Korea Advanced Institute of Science and Technology, Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea

  • Venue:
  • HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
  • Year:
  • 2011

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Abstract

We present 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. The readers can attain balanced understanding on the contention, free from a specific biased view. We applied a modified version of HITS algorithm and an SVM classifier trained with pseudo-relevant data for article analysis.