AAAI Publications, Third International AAAI Conference on Weblogs and Social Media

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RevRank: A Fully Unsupervised Algorithm for Selecting the Most Helpful Book Reviews
Oren Tsur, Ari Rappoport

Last modified: 2009-07-07


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.


review ranking; review helpfulness; content analysis; information systems applications

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