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Automatically assessing review helpfulness

Soo-Min Kim, Patrick Pantel, Timothy Chklovski, Marco Pennacchiotti

📖 Conference on Empirical Methods in Natural Language Processing (EMNLP) 📅 2006-01-01 🔗 DOI: 10.3115/1610075.1610135

📄 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.

📄 Abstract

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