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

Authors

Oren Tsur,Ari Rappoport

The Hebrew University,The Hebrew University


Proceedings:

Proceedings of the International AAAI Conference on Web and Social Media, 3

Volume

Issue:

Vol. 3 No. 1 (2009): Third International AAAI Conference on Weblogs and Social Media

Track:

Full Papers

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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.

DOI:

10.1609/icwsm.v3i1.13945


ICWSM

Vol. 3 No. 1 (2009): Third International AAAI Conference on Weblogs and Social Media