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

Jun Seok Kang (), Polina Kuznetsova (), Yejin Choi () and Michael Luca ()
Additional contact information
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

No 14-007, Harvard Business School Working Papers from Harvard Business School

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.

Pages: 6 pages
Date: 2013-07
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.hbs.edu/faculty/pages/download.aspx?name=14-007.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hbs:wpaper:14-007

Access Statistics for this paper

More papers in Harvard Business School Working Papers from Harvard Business School Contact information at EDIRC.
Bibliographic data for series maintained by HBS ().