Identifying leading indicators of product recalls from online reviews using positive unlabeled learning and domain adaptation
This work addresses consumer protection by enabling early detection of product hazards, though it is incremental as it builds on existing methods for data-scarce classification tasks.
The paper tackled the problem of identifying hazardous products from online reviews by proposing a system that mines Amazon reviews using positive unlabeled learning and domain adaptation, achieving an 8% absolute F1 score improvement over baselines and identifying safety hazard reviews prior to recalls for 45% of products.
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.