RevRank: A Fully Unsupervised Algorithm for Selecting the Most Helpful Book Reviews
Proceedings of the International AAAI Conference on Web and Social MediaPublished 19 March 2009Open access
Oren Tsur, Ari Rappoport
Citations136
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TL;DR
An algorithm for automatically ranking user-generated book reviews according to review helpfulness and shows that RevRank clearly outperforms a baseline imitating the Amazon user vote review ranking system.
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.
Keywords
Computer Science
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