Comparing different methods for opinion mining in newspaper articles

Authors:
Thomas Scholz;Stefan Conrad;Isabel Wolters
Affiliations:
Institute of Computer Science, Heinrich-Heine-University, Düsseldorf, Germany;Institute of Computer Science, Heinrich-Heine-University, Düsseldorf, Germany;Development Department, pressrelations, Düsseldorf, Germany
Venue:
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
Year:
2012

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Abstract

Adapting opinion mining for news articles is a challenging field and at the same time it is very interesting for many analyses, applications and systems in the field of media monitoring. In this paper, we illustrate specifics in this area in comparison with sentiment analysis of product reviews. Likewise, we introduce new methods for the determination of the sentiment polarity in statements, which are extracted from news articles. Our evaluation on a real world data set of a German Media Response Analysis (MRA) shows that these methods perform better than existing approaches and resources.