Analysis of Fine Dining Restaurant Reviews for Perception of Customers' Restaurant Service Quality

Abstract

The purpose of this study is to model the perception of customers’ service quality in fine dining\rrestaurants (FDRs) and to determine customer sentiments towards the service quality. I analyzed\r22,104 reviews of 25 restaurants on TripAdvisor through Aspect-Based Sentiment Analysis\r(ABSA). In terms of n-gram language models, the classification performance of sentiment polarity\rwas tested with Support Vector Machine (SVM), Naive Bayes (NB), C4.5, and Gradient Boosted\rTrees (GBT). I compared the performance of the model with Cohen’s kappa, accuracy, precision,\rrecall, and F-measure results. I found five topic models service, experience, surprise, taste, and\rfood kind by using latent Dirichlet allocation (LDA). In sentiment classification, SVM achieved\rthe best results in bigram with 74.5% average F-measure, 94.4% accuracy, and 49.2% kappa\rresults. This study contributes to the elements related to the perception of service quality in FDRs\rwith psychological quality proposed by the surprise topic. This is one of the few studies conducted\rwith ABSA on the perception of service quality in FDRs, and it is the first study examining the\rissue in terms of n-gram language models.