Predictive analytics using text classification for restaurant inspections

Abstract

According to the Center for Disease Control (CDC), there are almost 48 million people affected by foodborne diseases in the U.S. every year, including 3,000 deaths. The most effective way of avoiding food poisoning would be its prevention. However, complete prevention is not possible, therefore Public Health departments perform routine restaurant inspections, combined with the practice of inspecting specific restaurants once a disease outbreak is identified. Following other health applications (e.g., prediction of a flu outbreak using Twitter), we use social media and a predictive analytics approach to identify the need for targeted visits by city inspectors.