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

Oren Tsur, Ari Rappoport

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

76 Scopus citations

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.

Original languageAmerican English
Title of host publicationProceedings of the 3rd International AAAI Conference on Weblogs and Social Media, ICWSM 2009
PublisherAAAI Press
Pages154-161
Number of pages8
ISBN (Electronic)9781577354215
DOIs
StatePublished - 20 May 2009
Event3rd International AAAI Conference on Weblogs and Social Media, ICWSM 2009 - San Jose, United States
Duration: 17 May 200920 May 2009

Publication series

NameProceedings of the 3rd International AAAI Conference on Weblogs and Social Media, ICWSM 2009

Conference

Conference3rd International AAAI Conference on Weblogs and Social Media, ICWSM 2009
Country/TerritoryUnited States
CitySan Jose
Period17/05/0920/05/09

Bibliographical note

Publisher Copyright:
Copyright © 2009, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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