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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Probabilistic Ranking of Product Features from Customer Reviews
verfasst von : Lisette García-Moya, Henry Anaya-Sánchez, Rafel Berlanga, María José Aramburu
Erschienen in: Pattern Recognition and Image Analysis
Verlag: Springer Berlin Heidelberg
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In this paper, we propose a methodology for obtaining a probabilistic ranking of product features from a customer review collection. Our approach mainly relies on an entailment model between opinion and feature words, and suggest that in a probabilistic opinion model of words learned from an opinion corpus, feature words must be the most probable words generated from that model (even more than opinion words themselves). In this paper, we also devise a new model for ranking corpus-based opinion words. We have evaluated our approach on a set of customer reviews of five products obtaining encouraging results.