Journal article
A semantic measure of online review helpfulness and the importance of message entropy
Decision Support Systems, v 125, 113117
Oct 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.
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Details
- Title
- A semantic measure of online review helpfulness and the importance of message entropy
- Creators
- Jorge E. Fresneda - New Jersey Institute of TechnologyDavid Gefen - Drexel University
- Publication Details
- Decision Support Systems, v 125, 113117
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:000487568400008
- Scopus ID
- 2-s2.0-85070081932
- Other Identifier
- 991019168552604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems
- Operations Research & Management Science