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

Document Type

Article

Publication Date

10-1-2019

Abstract

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.

Identifier

85070081932 (Scopus)

Publication Title

Decision Support Systems

External Full Text Location

https://doi.org/10.1016/j.dss.2019.113117

ISSN

01679236

Volume

125

This document is currently not available here.

Share

COinS