Using text analysis to target government inspections
Subtitle: evidence from restaurant hygiene inspections and online reviews
By: Jun Seok Kang, Yejin Choi, Polina Kuznetsova, Michael Luca
Contributors: Choi, Yejin; Kuznetsova, Polina; Luca, Michael; Harvard Business School
Series: Working paper / Harvard Business School -- 14-007; Working paper (Harvard Business School) -- 14-007
Publisher: Harvard Business School
Published: 2013
Place of publication: Boston]
Language: eng
Description: 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 Natural Language Processing (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.
Notes: "July 2013."--Publisher's Web site.

Includes bibliographical references (page 5).