Though polarity classification has been extensively explored at various text levels and domains, there has been only comparatively little work looking into topic-related polarity classification. This paper takes a detailed look at how sentences expressing a polar attitude towards a given topic can be retrieved from a blog collection. A cascade of independent text classifiers on top of a sentence-retrieval engine is a solution with limited effectiveness. We show that more sophisticated processing is necessary. In this context, we not only investigate the impact of a more precise detection and disambiguation of polar expressions beyond simple text classification but also inspect the usefulness of a joint analysis of topic terms and polar expressions. In particular, we examine whether any syntactic information is beneficial in this classification task. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Wiegand, M., & Klakow, D. (2009). Topic-related polarity classification of blog sentences. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5816 LNAI, pp. 658–669). https://doi.org/10.1007/978-3-642-04686-5_54
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