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Abstract
News articles often reflect an opinion or point of view, with certain topics evoking more diverse opinions than others. For analyzing and better understanding public discourses, identifying such contested topics constitutes an interesting research question. In this paper, we describe an approach that combines NLP techniques and background knowledge from DBpedia for fi nding disputed topics in news sites. To identify these topics, we annotate each article with DBpedia concepts, extract their categories, and compute a sentiment score in order to identify those categories revealing signifi cant deviations in polarity across diff erent media. We illustrate our approach in a qualitative evaluation on a sample of six popular British and American news sites.
Keywords
DBpedia, Linked Open Data, Sentiment Analysis, Online News

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Please use this url to cite or link to this publication:

MLA
De Clercq, Orphée, et al. “Identifying Disputed Topics in the News.” Proceedings of the LD4KD Workshop at ECML/PKDD2014, CEUR, 2014, pp. 37–48.
APA
De Clercq, O., Hertling, S., Hoste, V., Ponzetto, S. P., & Paulheim, H. (2014). Identifying disputed topics in the news. In Proceedings of the LD4KD Workshop at ECML/PKDD2014 (pp. 37–48). Nancy, France: CEUR.
Chicago author-date
De Clercq, Orphée, Sven Hertling, Veronique Hoste, Simone Paolo Ponzetto, and Heiko Paulheim. 2014. “Identifying Disputed Topics in the News.” In Proceedings of the LD4KD Workshop at ECML/PKDD2014, 37–48. CEUR.
Chicago author-date (all authors)
De Clercq, Orphée, Sven Hertling, Veronique Hoste, Simone Paolo Ponzetto, and Heiko Paulheim. 2014. “Identifying Disputed Topics in the News.” In Proceedings of the LD4KD Workshop at ECML/PKDD2014, 37–48. CEUR.
Vancouver
1.
De Clercq O, Hertling S, Hoste V, Ponzetto SP, Paulheim H. Identifying disputed topics in the news. In: Proceedings of the LD4KD Workshop at ECML/PKDD2014. CEUR; 2014. p. 37–48.
IEEE
[1]
O. De Clercq, S. Hertling, V. Hoste, S. P. Ponzetto, and H. Paulheim, “Identifying disputed topics in the news,” in Proceedings of the LD4KD Workshop at ECML/PKDD2014, Nancy, France, 2014, pp. 37–48.
@inproceedings{5673909,
  abstract     = {{News articles often reflect an opinion or point of view, with
certain topics evoking more diverse opinions than others. For analyzing and better understanding public discourses, identifying such contested topics constitutes an interesting research question. In this paper, we describe an approach that combines NLP techniques and background knowledge from DBpedia for finding disputed topics in news sites. To identify these topics, we annotate each article with DBpedia concepts, extract their categories, and compute a sentiment score in order to identify those categories revealing significant deviations in polarity across different media. We illustrate our approach in a qualitative evaluation on a sample of six popular British and American news sites.}},
  author       = {{De Clercq, Orphée and Hertling, Sven and Hoste, Veronique and Ponzetto, Simone Paolo and Paulheim, Heiko}},
  booktitle    = {{Proceedings of the LD4KD Workshop at ECML/PKDD2014}},
  issn         = {{1613-0073}},
  keywords     = {{DBpedia,Linked Open Data,Sentiment Analysis,Online News}},
  language     = {{eng}},
  location     = {{Nancy, France}},
  pages        = {{37--48}},
  publisher    = {{CEUR}},
  title        = {{Identifying disputed topics in the news}},
  url          = {{http://ceur-ws.org/Vol-1232/paper4.pdf}},
  year         = {{2014}},
}