Consumer protection agencies are charged with safeguarding the public from hazardous products, but the thousands of products under their jurisdiction make it challenging to identify and respond to consumer complaints quickly. From the consumer's perspective, online reviews can provide evidence of product defects, but manually sifting through hundreds of reviews is not always feasible. In this paper, we propose a system to mine Amazon.com reviews to identify products that may pose safety or health hazards. Since labeled data for this task are scarce, our approach combines positive unlabeled learning with domain adaptation to train a classifier from consumer complaints submitted to the U.S. Consumer Product Safety Commission. On a validation set of manually annotated Amazon product reviews, we find that our approach results in an absolute F1 score improvement of 8 over the best competing baseline. Furthermore, we apply the classifier to Amazon reviews of known recalled products; the classifier identifies reviews reporting safety hazards prior to the recall date for 45 of the products. This suggests that the system may be able to provide an early warning system to alert consumers to hazardous products before an official recall is announced.
Very recently, winkler2016toy used a keyword based approach to identify online reviews that report injuries from toy products. In addition to the manual effort required to curate the keyword list, the approach appears to produce low precision rates (9-44 sixteen mentioned an injury. The authors apply the same approach to detect defects in dishwashers, with similar precision values @cite_0 . In contrast, our proposed approach fits a statistical classifier with no human intervention required, resulting in $>85 Other recent work has identified vehicle defects in consumer reviews using standard text classification, with accuracies ranging from 62 not feasible to annotate sufficient messages to use standard supervised learning. Additionally, zhang2015predicting built an unsupervised approach to clustering vehicle defects by subcategory. Such a method may serve to complement our present work by providing more fine-grained clusters of reviews by hazard type.
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The recent surge in the usage of social media has created an enormous amount of user-generated content (UGC). While there are streams of research that seek to mine UGC, these research studies seldom tackle analysis of this textual content from a quality management perspective. In this study, we synthesize existing research studies on text mining and propose an integrated text analytic framework for product defect discovery. The framework effectively leverages rich social media content and quantifies the text using various automatically extracted signal cues. These extracted signal cues can then be used as modeling inputs for product defect discovery. We showcase the usefulness of the framework by performing product defect discovery using UGC in both the automotive and the consumer electronics domains. We use principal component analysis and logistic regression to produce a multivariate explanatory analysis relating defects to quantitative measures derived from text. For our samples, we find that a selection of distinctive terms, product features, and semantic factors are strong indicators of defects, whereas stylistic, social, and sentiment features are not. For high sales volume products, we demonstrate that significant corporate value is derivable from a reduction in defect discovery time and consequently defective product units in circulation.