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Exploiting social context for review quality prediction

Published: 26 April 2010 Publication History

Abstract

Online reviews in which users publish detailed commentary about their experiences and opinions with products, services, or events are extremely valuable to users who rely on them to make informed decisions. However, reviews vary greatly in quality and are constantly increasing in number, therefore, automatic assessment of review helpfulness is of growing importance. Previous work has addressed the problem by treating a review as a stand-alone document, extracting features from the review text, and learning a function based on these features for predicting the review quality. In this work, we exploit contextual information about authors' identities and social networks for improving review quality prediction. We propose a generic framework for incorporating social context information by adding regularization constraints to the text-based predictor. Our approach can effectively use the social context information available for large quantities of unlabeled reviews. It also has the advantage that the resulting predictor is usable even when social context is unavailable. We validate our framework within a real commerce portal and experimentally demonstrate that using social context information can help improve the accuracy of review quality prediction especially when the available training data is sparse.

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cover image ACM Other conferences
WWW '10: Proceedings of the 19th international conference on World wide web
April 2010
1407 pages
ISBN:9781605587998
DOI:10.1145/1772690

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 April 2010

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Author Tags

  1. graph regularization
  2. review helpfulness
  3. review quality
  4. social network

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WWW '10
WWW '10: The 19th International World Wide Web Conference
April 26 - 30, 2010
North Carolina, Raleigh, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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  • (2024)LexiSNTAGMM: an unsupervised framework for sentiment classification in data from distinct domains, synergistically integrating dictionary-based and machine learning approachesSocial Network Analysis and Mining10.1007/s13278-024-01268-z14:1Online publication date: 18-May-2024
  • (2024)Apple doesn’t fall far from the tree: Effect of extrinsic factors of online reviews on predicting useless reviewsElectronic Commerce Research10.1007/s10660-024-09919-1Online publication date: 7-Nov-2024
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  • (2023)Revisiting online reviews: signals of latent reviewer traits mediate the review length-helpfulness relationshipJournal of Marketing Theory and Practice10.1080/10696679.2023.219663632:3(346-361)Online publication date: 7-Apr-2023
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