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The ability to detect and rapidly respond to the presence of safety defects is vital to firms and to regulatory agencies. In this paper, we employ a text mining methodology to generate industry-specific “smoke terms” for identifying these defects in the countertop appliances and over-the-counter medicine industries. Building upon prior work, we propose several methodological improvements to enhance the precision of our industry-specific terms. First, we replace the subjective manual curation of these terms with an automated Tabu search algorithm, which provides a statistically significant improvement over a sample of human-curated lists. Contrary to the assumptions of prior work, we find that shorter, targeted smoke term lists produce superior precision. Second, we incorporate non-textual review features to enhance the performance of these smoke term lists. In total, we find greater than a twofold improvement over …