Low-Quality Product Review Detection in Opinion Summarization
Published 1 June 2007
Jingjing Liu, Yunbo Cao, Chin-Yew Lin, Yalou Huang, Ming Zhou
Citations392
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TL;DR
Experimental results show that the proposed approach effectively discriminates lowquality reviews from high-quality ones and enhances the task of opinion summarization by detecting and filtering low quality reviews.
Abstract
Product reviews posted at online shopping sites vary greatly in quality. This paper ad-dresses the problem of detecting low-quality product reviews. Three types of bi-ases in the existing evaluation standard of product reviews are discovered. To assess the quality of product reviews, a set of spe-cifications for judging the quality of re-views is first defined. A classification-based approach is proposed to detect the low-quality reviews. We apply the pro-posed approach to enhance opinion sum-marization in a two-stage framework. Ex-perimental results show that the proposed approach effectively (1) discriminates low-quality reviews from high-quality ones and (2) enhances the task of opinion summari-zation by detecting and filtering low-quality reviews. 1
Keywords
Computer Science
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