Elisabeth Lex
Andreas Juffinger
Michael Granitzer
ABSTRACT
In this work, we assess objectivity in online news media. We propose to use topic independent features and we show in a cross-domain experiment that with standard bag-of-word models, classifiers implicitly learn topics. Our experiments revealed that our methodology can be applied across different topics with consistent classification performance.
- H. Guan, J. Zhou, and M. Guo. A class-feature-centroid classifer for text categorization. In WWW '09: Proceedings of the 18th international conference on World wide web, pages 201-210, New York, NY, USA, 2009. ACM. Google Scholar
Digital Library
- E. Lex, M. Granitzer, M. Muhr, and A. Juffinger. Stylometric features for emotion level classification in news related blogs. In Proceedings of the 9th RIAO Conference (RIAO 2010), 2010. Google ScholarDigital Library
- T. Wilson, J. Wiebe, and P. Hoymann. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of HLT/EMNLP, 2005. Google ScholarDigital Library
Index Terms
- Objectivity classification in online media
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