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 ()
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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
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Persistent link: https://EconPapers.repec.org/RePEc:spr:infosf:v:24:y:2022:i:4:d:10.1007_s10796-021-10122-y
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DOI: 10.1007/s10796-021-10122-y
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