A semantic measure of online review helpfulness and the importance of message entropy

The helpfulness of online reviews and their impact on purchase decisions is well established. Much previous research measured that helpfulness by analyzing vote assessments. This study examines an alternative semantic measure based on a text analysis of the term “helpful” in those reviews. Analyzing...

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Bibliographic Details
Published in:Decision Support Systems 2019-10, Vol.125, p.113117, Article 113117
Main Authors: Fresneda, Jorge E., Gefen, David
Format: Article
Language:English
Subjects:
Assessments
Ecommerce
Entropy
Information entropy increment
Latent semantic analysis
Online consumer reviews
Review helpfulness
Semantics
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Summary:The helpfulness of online reviews and their impact on purchase decisions is well established. Much previous research measured that helpfulness by analyzing vote assessments. This study examines an alternative semantic measure based on a text analysis of the term “helpful” in those reviews. Analyzing over 20,000 reviews shows that the semantic measure has a considerably higher R2 than vote assessments. Moreover, the new measure, as opposed to those based on votes, is not affected by posting order, avoiding a known source of bias in vote measures, and is conceptually unrelated to the number of previous helpfulness evaluations. The study also examines the role of the incremental entropy of each review's content as a new determinant of both the existing measures and the new semantic measure of online review helpfulness. The potential of the semantic measure, including that it can be automatically calculated even before human review users read the review, is discussed.
ISSN:0167-9236
1873-5797
DOI:10.1016/j.dss.2019.113117