Abstract
We present an algorithm for automatically ranking user-generated book reviews according to review helpfulness. Given a collection of reviews, our REVRANK algorithm identifies a lexicon of dominant terms that constitutes the core of a virtual optimal review. This lexicon defines a feature vector representation. Reviews are then converted to this representation and ranked according to their distance from a 'virtual core' review vector. The algorithm is fully unsupervised and thus avoids costly and error-prone manual training annotations. Our experiments show that REVRANK clearly outperforms a baseline imitating the Amazon user vote review ranking system.
Cite
CITATION STYLE
Tsur, O., & Rappoport, A. (2009). REVRANK: A Fully Unsupervised Algorithm for Selecting the Most Helpful Book Reviews. In Proceedings of the 3rd International AAAI Conference on Weblogs and Social Media, ICWSM 2009 (Vol. 3, pp. 154–161). AAAI Press. https://doi.org/10.1609/icwsm.v3i1.13945
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
