Ranking Product Reviews
(English)Article in journal (Other academic) Submitted
E-commerce Web sites owe much of their popularity to consumer reviews provided together with productdescriptions. On-line customers spend hours and hours going through heaps of textual reviews to buildconfidence in products they are planning to buy. At the same time, popular products have thousands of user-generated reviews. Current approaches to present them to the user or recommend an individual review for aproduct are based on the helpfulness or usefulness of each review. In this paper we look at the top-k reviewsin a ranking to give a good summary to the user with each review complementing the others. To this endwe use Latent Dirichlet Allocation to detect latent topics within reviews and make use of the assigned starrating for the product as an indicator of the polarity expressed towards the product and the latent topicswithin the review. We present a framework to cover different ranking strategies based on the user’s need:Summarizing all reviews; focus on a particular latent topic; or focus on positive, negative or neutral aspects.We evaluated the system using manually annotated review data from a commercial review Web site.
Ranking, Topic Models, Summarization, Diversification, Review Recommendation
IdentifiersURN: urn:nbn:se:kth:diva-118474OAI: oai:DiVA.org:kth-118474DiVA: diva2:606429
2011 IEEE/WIC/ACM International Conference on Web Intelligence (WI’11)
QS 20132013-02-192013-02-192014-06-24Bibliographically approved