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Consumer protection agencies are charged with safeguarding the public fromhazardous products, but the thousands of products under their jurisdiction makeit challenging to identify and respond to consumer complaints quickly. From theconsumer's perspective, online reviews can provide evidence of product defects,but manually sifting through hundreds of reviews is not always feasible. Inthis paper, we propose a system to mine Amazon.com reviews to identify productsthat may pose safety or health hazards. Since labeled data for this task arescarce, our approach combines positive unlabeled learning with domainadaptation to train a classifier from consumer complaints submitted to the U.S.Consumer Product Safety Commission. On a validation set of manually annotatedAmazon product reviews, we find that our approach results in an absolute F1score improvement of 8% over the best competing baseline. Furthermore, we applythe classifier to Amazon reviews of known recalled products; the classifieridentifies reviews reporting safety hazards prior to the recall date for 45% ofthe products. This suggests that the system may be able to provide an earlywarning system to alert consumers to hazardous products before an officialrecall is announced.

Identifying leading indicators of product recalls from online reviews using positive unlabeled learning and domain adaptation