Exploiting social context for review quality prediction

Online reviews in which users publish detailed commentary about their experiences and opinions with products, services, or events are extremely valuable to users who rely on them to make informed decisions. However, reviews vary greatly in quality and are constantly increasing in number, therefore, automatic assessment of review helpfulness is of growing importance. Previous work has addressed the problem by treating a review as a stand-alone document, extracting features from the review text, and learning a function based on these features for predicting the review quality. In this work, we exploit contextual information about authors’ identities and social networks for improving review quality prediction. We propose a generic framework for incorporating social context information by adding regularization constraints to the text-based predictor. Our approach can effectively use the social context information available for large quantities of unlabeled reviews. It also has the advan...

• Type: Conference
• Year: 2010
• Where: WWW
• Authors: Yue Lu, Panayiotis Tsaparas, Alexandros Ntoulas, Livia Polanyi
• Keywords: Internet Technology, Review, Review Quality, Social Context Information, WWW 2010