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Using Text Analysis to Target Government Inspections: Evidence from Restaurant Hygiene Inspections and Online Reviews

Author

Listed:
  • Jun Seok Kang

    (Department of Computer Science, Stony Brook University)

  • Polina Kuznetsova

    (Department of Computer Science, Stony Brook University)

  • Yejin Choi

    (Department of Computer Science, Stony Brook University)

  • Michael Luca

    (Harvard Business School, Negotiation, Organizations & Markets Unit)

Abstract

Restaurant hygiene inspections are often cited as a success story of public disclosure. Hygiene grades influence customer decisions and serve as an accountability system for restaurants. However, cities (which are responsible for inspections) have limited resources to dispatch inspectors, which in turn limits the number of inspections that can be performed. We argue that NLP can be used to improve the effectiveness of inspections by allowing cities to target restaurants that are most likely to have a hygiene violation. In this work, we report the first empirical study demonstrating the utility of review analysis for predicting restaurant inspection results.

Suggested Citation

  • Jun Seok Kang & Polina Kuznetsova & Yejin Choi & Michael Luca, 2013. "Using Text Analysis to Target Government Inspections: Evidence from Restaurant Hygiene Inspections and Online Reviews," Harvard Business School Working Papers 14-007, Harvard Business School.
    • Handle: RePEc:hbs:wpaper:14-007
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