Automatically assessing review helpfulness
Published 1 January 2006Open access
Soo-Min Kim, Patrick Pantel, Tim Chklovski, Marco Pennacchiotti
Citations522
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TL;DR
This paper considers the task of automatically assessing review helpfulness, and finds that the most useful features include the length of the review, its unigrams, and its product rating.
Abstract
User-supplied reviews are widely and increasingly used to enhance e-commerce and other websites. Because reviews can be numerous and varying in quality, it is important to assess how helpful each review is. While review helpfulness is currently assessed manually, in this paper we consider the task of automatically assessing it. Experiments using SVM regression on a variety of features over Amazon.com product reviews show promising results, with rank correlations of up to 0.66. We found that the most useful features include the length of the review, its unigrams, and its product rating.
Keywords
Computer Science
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