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

Author

Listed:
  • 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
    Centre of Excellence in Operations and Information Management, Thammasat Business School)

  • Peter Ractham

    (Thammasat University)

  • Michelle M.H. Şeref

    (Virginia Tech)

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.

Suggested Citation

  • Nohel Zaman & David M. Goldberg & Richard J. Gruss & Alan S. Abrahams & Siriporn Srisawas & Peter Ractham & Michelle M.H. Şeref, 2022. "Cross-Category Defect Discovery from Online Reviews: Supplementing Sentiment with Category-Specific Semantics," Information Systems Frontiers, Springer, vol. 24(4), pages 1265-1285, August.
    • Handle: RePEc:spr:infosf:v:24:y:2022:i:4:d:10.1007_s10796-021-10122-y
    DOI: 10.1007/s10796-021-10122-y
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    References listed on IDEAS

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