PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

RevRank: A Fully Unsupervised Algorithm for Selecting the Most Helpful Book Reviews
Yosef Solewicz and Ari Rappoport
AAAI Conference on Weblogs and Social Media - ICWSM-09 2009.

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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
Information Retrieval & Textual Information Access
ID Code:6813
Deposited By:Oren Tsur
Deposited On:08 March 2010