Title: Product retrieval based on semantic similarity of consumer reviews to natural language query

Authors: Kenji Sugiki, Shigeki Matsubara

Addresses: Department of Systems and Social Informatics, Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan. ' Information Technology Center, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan

Abstract: In this paper, we propose a product retrieval method using product reviews. The method calculates the score of a product based on the similarity between a user|s request extracted from a natural language query and consumers| opinions extracted from reviews of the product. We also propose the method to generate the thesaurus automatically from consumers| reviews. The thesaurus expresses semantic similarities between opinion expressions. We implemented an accommodation retrieval system and constructed the opinion thesaurus automatically from 652,875 reviews. The experimental results show the effectiveness of our method.

Keywords: natural language processing; NLP; information retrieval; thesaurus; sentiment analysis; opinion mining; product retrieval; semantic similarity; consumer reviews; natural language queries; product reviews; consumer opinions.

DOI: 10.1504/IJKWI.2010.034188

International Journal of Knowledge and Web Intelligence, 2010 Vol.1 No.3/4, pp.209 - 226

Published online: 17 Jul 2010 *

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