In this study, we develop a model that assesses product risk using online reviews from Amazon.com. We first identify unique words and phrases capable of identifying hazards. Second, we estimate risk severity using hazard type weights and risk likelihood using total reviews as a proxy for sales volume. In addition, we obtain expert assessments of product hazard risk (risk likelihood and severity) from a sample of high- and low-risk consumer products identified by a computerized risk assessment model we have developed. Third, we assess the validity of our computerized product risk assessment scoring model by utilizing the experts' survey responses. We find that our model is especially consistent with expert judgments of hazard likelihood but not as consistent with expert judgments of hazard severity. This model helps organizations to determine the risk severity, risk likelihood, and overall risk level of a specific product. The model produced by this study is helpful for product safety practitioners in product risk identification, characterization, and mitigation.
Keywords: product safety; risk assessment; text mining.
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