Paper: Automatically Assessing Review Helpfulness

ACL ID W06-1650
Title Automatically Assessing Review Helpfulness
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006
Authors

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 manu- ally, in this paper we consider the task of automatically assessing it. Experi- ments using SVM regression on a vari- ety 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 in- clude the length of the review, its uni- grams, and its product rating.