Designing Novel Review Ranking Systems: Predicting Usefulness and Impact of Reviews

With the rapid growth of the Internet, users’ ability to publish content has created active electronic communities that provide a wealth of product information.  Consumers naturally gravitate to reading reviews in order to decide whether to buy a product.  However, the high volume of reviews that are typically published for a single product makes it harder for individuals to locate the best reviews and understand the true underlying quality of a product based on the reviews.  Similarly, the manufacturer of a product needs to identify the reviews that influence the customer base, and examine the content of these reviews.  In this paper, we propose two ranking mechanisms for ranking product reviews: a consumer-oriented ranking mechanism ranks the reviews according to their expected helpfulness, and a manufacturer-oriented ranking mechanism ranks the reviews according to their expected effect on sales.  Our ranking mechanism combines econometric analysis with text mining techniques in general, and with subjectivity analysis in particular.  We show that subjectivity analysis can give useful clues about the helpfulness of a review and about its impact on sales. Our results can have several implications for the market design of online opinion forums.