基于正向无标记学习和领域自适应的在线评论产品召回领先指标识别
Identifying leading indicators of product recalls from online reviews using positive unlabeled learning and domain adaptation
摘要：
消费者保护机构负责保护公众免受有害产品的侵害，但其管辖范围内的数千种产品使其难以快速识别和回应消费者投诉。从消费者的角度来看，在线评论可以提供产品缺陷的证据，但手动筛选数百条评论并不总是可行的。在本文中，我们提出了一个系统来挖掘Amazon.com的评论，以确定产品，可能会造成安全或健康危害。由于这项任务的标记数据是稀缺的，我们的方法结合了积极的未标记学习领域领域自适应训练分类器从消费者投诉提交给美国消费品安全委员会。在手动注释的亚马逊产品评论的验证集上，我们发现我们的方法比最佳竞争基线的绝对F1分数提高了8%。此外，我们将分类器应用于已知召回产品的亚马逊评论;分类器识别了45%产品在召回日期之前报告安全隐患的评论。这表明，该系统可能能够提供一个早期预警系统，在官方宣布召回之前提醒消费者注意危险产品。
英文摘要：
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.
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