Aspect-based sentiment analysis: Jamie’s Italian restaurant case study

Abstract

Consumers use technologies to share their experiences, leading to the creation of online platforms where the main objective is to allow users to share their opinion about products or services, such as hotels, books, restaurants, and search for the opinions of other users. The emergence of these online platforms has changed the business dynamics, the restaurant sector was no exception. The main goal of this work is to understand how different factors impact the final review rating of a restaurant, using two Jamie Oliver restaurants as a case study. A model was applied that allows us to identify such factors and their associated sentiment through text mining methods. Using this model, it was possible to understand which factors influence the rating the most. Results show that the factors most mentioned in the reviews were ‘food’ and ‘service’ and the least mentioned were ‘atmosphere’ and ‘location’.