Cross-Category Defect Discovery from Online Reviews: Supplementing Sentiment with Category-Specific Semantics

Nohel Zaman (), David M. Goldberg (), Richard J. Gruss (), Alan S. Abrahams (), Siriporn Srisawas (), Peter Ractham () and Michelle M.H. Şeref ()
Additional contact information
Nohel Zaman: Loyola Marymount University
David M. Goldberg: San Diego State University
Richard J. Gruss: Radford University
Alan S. Abrahams: Virginia Tech
Siriporn Srisawas: Thammasat University
Peter Ractham: Thammasat University
Michelle M.H. Şeref: Virginia Tech

Information Systems Frontiers, 2022, vol. 24, issue 4, No 12, 1265-1285

Abstract: Abstract Online reviews contain many vital insights for quality management, but the volume of content makes identifying defect-related discussion difficult. This paper critically assesses multiple approaches for detecting defect-related discussion, ranging from out-of-the-box sentiment analyses to supervised and unsupervised machine-learned defect terms. We examine reviews from 25 product and service categories to assess each method’s performance. We examine each approach across the broad cross-section of categories as well as when tailored to a singular category of study. Surprisingly, we found that negative sentiment was often a poor predictor of defect-related discussion. Terms generated with unsupervised topic modeling tended to correspond to generic product discussions rather than defect-related discussion. Supervised learning techniques outperformed the other text analytic techniques in our cross-category analysis, and they were especially effective when confined to a single category of study. Our work suggests a need for category-specific text analyses to take full advantage of consumer-driven quality intelligence.

Keywords: Text analytics; Sentiment analysis; Quality management; Supervised learning; Unsupervised learning; Business intelligence (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10796-021-10122-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:infosf:v:24:y:2022:i:4:d:10.1007_s10796-021-10122-y

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10796

DOI: 10.1007/s10796-021-10122-y

Access Statistics for this article

Information Systems Frontiers is currently edited by Ram Ramesh and Raghav Rao

More articles in Information Systems Frontiers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().