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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 …