Abstract
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.
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García-Moya, L., Anaya-Sánchez, H., Berlanga, R., Aramburu, M.J. (2011). Probabilistic Ranking of Product Features from Customer Reviews. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21257-4_26
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DOI: https://doi.org/10.1007/978-3-642-21257-4_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21256-7
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