Modeling and Predicting the Helpfulness of Online Reviews

Online reviews provide a valuable resource for potential customers to make purchase decisions. However, the sheer volume of available reviews as well as the large variations in the review quality present a big impediment to the effective use of the reviews, as the most helpful reviews may be buried...

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Bibliographic Details
Published in2008 Eighth IEEE International Conference on Data Mining pp. 443 - 452
Main Authors Liu, Yang, Huang, Xiangji, An, Aijun, Yu, Xiaohui
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2008
Subjects
Computer science
Data engineering
Data mining
Helpfulness prediction
Impedance
Information technology
Motion pictures
Prediction algorithms
Predictive models
Review mining
Voting
Writing
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Summary:Online reviews provide a valuable resource for potential customers to make purchase decisions. However, the sheer volume of available reviews as well as the large variations in the review quality present a big impediment to the effective use of the reviews, as the most helpful reviews may be buried in the large amount of low quality reviews. The goal of this paper is to develop models and algorithms for predicting the helpfulness of reviews, which provides the basis for discovering the most helpful reviews for given products. We first show that the helpfulness of a review depends on three important factors: the reviewerpsilas expertise, the writing style of the review, and the timeliness of the review. Based on the analysis of those factors, we present a nonlinear regression model for helpfulness prediction. Our empirical study on the IMDB movie reviews dataset demonstrates that the proposed approach is highly effective.
ISBN:076953502X
9780769535029
ISSN:1550-4786
2374-8486
DOI:10.1109/ICDM.2008.94