A semiautomated risk assessment method for consumer products.

Area of Science:

• Consumer Product Safety
• Data Science
• Risk Management

Background:

• Assessing product risk is crucial for consumer safety and regulatory compliance.
• Existing methods for product risk assessment can be resource-intensive and may not capture real-time consumer feedback.
• Online reviews offer a rich source of data for understanding product hazards and risks.

Purpose of the Study:

• To develop and validate a novel model for assessing product risk using online consumer reviews.
• To identify unique linguistic indicators of product hazards from e-commerce platforms.
• To evaluate the model's consistency with expert assessments of risk likelihood and severity.

Main Methods:

• Natural Language Processing (NLP) techniques to identify hazard-related keywords and phrases in online reviews.
• Development of a scoring system for risk severity (hazard weights) and risk likelihood (review volume proxy for sales).
• Validation of the model by comparing its output with expert judgments on a curated set of consumer products.

Main Results:

• The developed model effectively identifies unique words and phrases associated with product hazards.
• The model demonstrates strong consistency with expert assessments of hazard likelihood.
• Consistency with expert judgments on hazard severity was found to be lower, indicating an area for future refinement.

Conclusions:

• The proposed model offers a viable method for product risk identification, characterization, and mitigation.
• This approach can assist organizations in proactively managing product safety by leveraging readily available online data.
• Further research can enhance the model's accuracy in assessing hazard severity for more comprehensive risk management.