LAUSR

Abstract The purpose of this research is to examine the effects of restaurant attributes and the underlying factors impacting overall customer experience within a range of different restaurant types. To understand their experiences, this study analyses online reviews of restaurants which have become important sources of customer experience data. This current research utilises a combination of quantitative analyses to examine 935,386 Google Maps reviews of 5010 restaurants in London, Birmingham, and Manchester. The authors used the VADER sentiment analysis algorithm to measure the sentiment of four key restaurant attributes: food, service, atmosphere, and value. Logistic regression was conducted to test the relationships between these attributes and a 5-star rating. Furthermore, logistic regression was used to compare the changes of odds at different star rating levels. To understand the factors that drive positive and negative reviews, the top 30 food items of 8 types of restaurants were analysed.