Analyzing and Filtering Food Items in Restaurant Reviews: Sentiment Analysis and Web Scraping


Nina Luo1, Caroline Kwan1, Yu Sun2, Fangyan Zhang3, 1USA, 2California State Polytechnic University, USA, 3ASML, USA


Online reviews now influence many purchasing decisions. However, the length and significance of these reviews vary, especially when reviewers have different criteria for making their assessments. In this paper, we present an efficient method for analyzing restaurant reviews on the popular review site known as Yelp. We have created an application that uses web scraping, natural language processing, and a blacklist to recommend customer favorite dishes from restaurants. To test the app, we have conducted a qualitative evaluation of the approach. Through analyzing two different ways to obtain Yelp reviews and evaluating our word filtering process, we have concluded that an average of 51% of nonfood words are filtered out by the blacklist we made. We provide further details of its deployment, user interface design, and comparison to the opinion mining field, which utilizes similar tools to make financial market predictions based on the perceived public opinion on social media.


Web scraping, natural language processing, flutter, iOS, android.

Full Text  Volume 10, Number 12