Paper: Where Not to Eat? Improving Public Policy by Predicting Hygiene Inspections Using Online Reviews

ACL ID D13-1150
Title Where Not to Eat? Improving Public Policy by Predicting Hygiene Inspections Using Online Reviews
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

This paper offers an approach for governments to harness the information contained in social media in order to make public inspections and disclosure more efficient. As a case study, we turn to restaurant hygiene inspections ? which are done for restaurants throughout the United States and in most of the world and are a fre- quently cited example of public inspections and disclosure. We present the first empiri- cal study that shows the viability of statistical models that learn the mapping between tex- tual signals in restaurant reviews and the hy- giene inspection records from the Department of Public Health. The learned model achieves over 82% accuracy in discriminating severe offenders from places with no violation, and provides insights into salient cues in reviews that are indicative...