Text analytics:not just for customer sentiment Posted on : Jul 03 - 2017

Sentiment analysis is one of the most prevalent uses of text analytics, but the technology has many other valuable uses. Text analytics finds a range of applications in scientific, medical and technology development. It can detect root causes of events and augment the knowledge of what happened with an understanding of why it happened. When used predictively, it can help anticipate future outcomes and prevent adverse events. Text analytics can also enable process automation and case management.

The Center for Food Safety and Applied Nutrition (CFSAN) is part of the Foods and Veterinary Medicine Program of the Food and Drug Administration. It is responsible for protecting public health by ensuring that the United States’ food supply (including dietary supplements) is safe, secure and properly labeled. It also ensures that cosmetics are safe and properly labeled. CFSAN regulates more than $400 billion worth of domestic food and more than $50 billion in imported foods, as well as about $60 billion worth of cosmetics. Globalization of the food supply and increased consumer demand for high quality and variety are driving a requirement for new approaches to meet standards and ensure consumer safety.

In 2013, the Center decided to investigate ways in which problematic chemicals could be identified before they got into the food supply. “We wanted to get ahead of the curve,” says Ernest Kwegyir-Afful, lead for post-market activities in the Division for Food Contact Notifications in the Office of Food Additive Safety (OFAS), which is a part of CFSAN. “Rather than waiting for each adverse event to occur, CFSAN wanted to be aware of the precursors and try to prevent the incident.”

A Food Advisory Committee was established, composed of toxicologists, chemists, public health officials and other experts to consider how to approach and solve the problem. The committee decided to look at different data streams that could provide information that could be used to develop predictive models. The idea was to determine which signals were indicative of a subsequent problem in the food supply chain. CFSAN subsequently started a centerwide Chemical Signal Detection program, led by the Signals Management Branch in the Office of Analytics and Outreach, leveraging expertise from all the program offices in the center. View More