An estimated 48 million cases of foodborne illness are contracted in the United States every year, causing about 128,000 hospitalizations and 3,000 deaths, according to the Centers for Disease Control (CDC). In some instances, the source is well known, such as a batch of tainted ground beef that infected 209 people with E. Coli in 2019. But 80 percent of food poisoning cases are of unknown origin, making it impossible to inform consumers of hazardous food items.
David Goldberg, assistant professor of management information systems at San Diego State University, wants to improve the traceability and communication of risky food products. In a new study published by the journal Risk Analysis, his research team proposes a new Food Safety Monitoring System (FSMS) that utilizes consumer comments posted on websites to identify products associated with food-related illnesses.
The researchers utilized an AI technology called text mining to analyze comments and reviews from two websites: Amazon.com, the world’s largest e-commerce retailer, and IWasPoisoned.com, a site where consumers alert others to cases of food poisoning. The database consisted of 11,190 randomly selected Amazon reviews of “grocery and canned food” items purchased between 2000 and 2018, along with 8,596 reviews of food products posted on IWasPoisoned.com. These two datasets allowed the researchers to test the text mining tools before analyzing 4.4 million more Amazon reviews.