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

Jun Seok Kang,Polina Kuznetsova,Michael Luca,Yejin Choi

Published 2013 in Conference on Empirical Methods in Natural Language Processing

ABSTRACT

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 frequently cited example of public inspections and disclosure. We present the first empirical study that shows the viability of statistical models that learn the mapping between textual signals in restaurant reviews and the hygiene 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 of the restaurant’s sanitary conditions. Our study suggests that public disclosure policy can be improved by mining public opinions from social media to target inspections and to provide alternative forms of disclosure to customers.

PUBLICATION RECORD

  • Publication year

    2013

  • Venue

    Conference on Empirical Methods in Natural Language Processing

  • Publication date

    2013-07-01

  • Fields of study

    Computer Science, Political Science

  • Identifiers
    DOI 10.2139/SSRN.2293165
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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