Using Text Analysis to Target Government Inspections: Evidence from Restaurant Hygiene Inspections and Online Reviews
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.
|Date of creation:||Jul 2013|
|Date of revision:|
|Contact details of provider:|| Postal: Soldiers Field, Boston, Massachusetts 02163|
Web page: http://www.hbs.edu/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:hbs:wpaper:14-007. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Soebagio Notosoehardjo)
If references are entirely missing, you can add them using this form.