Using Text Analysis to Target Government Inspections: Evidence from Restaurant Hygiene Inspections and Online Reviews
AbstractRestaurant 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.
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Bibliographic InfoPaper provided by Harvard Business School in its series Harvard Business School Working Papers with number 14-007.
Length: 6 pages
Date of creation: Jul 2013
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-07-20 (All new papers)
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