RevRank: a Fully Unsupervised Algorithm for Selecting the Most Helpful Book Reviews

author: Oren Tsur, Harvard School of Engineering and Applied Sciences, Harvard University
published: June 24, 2009,   recorded: May 2009,   views: 5059

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We present an algorithm for automatically ranking usergenerated 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.

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