Foreign Visitors’ Dining Experiences in Asian Restaurants Operating in Istanbul
DOI:
https://doi.org/10.21325/jotags.2022.1033Keywords:
Dining experience, Asian restaurant, Topic modeling, Sentiment analysis, Latent Dirichlet allocationAbstract
The purpose of this study is to reveal the dimensions of dining experience by modeling the foreign visitors reviews for Asian restaurants operating in Istanbul, Turkey. In the study, the latent Dirichlet allocation (LDA) algorithm, sentiment analysis, dimensional salience and valence analysis (DSVA), and lexicon salience and valence analysis (LSVA) were used as text mining methods to analyze 3,843 online English reviews for Asian restaurants on TripAdvisor. Five dimensions were found for the Asian restaurants experience: authenticity, staff, sushi, service, and view. According to the dimensional salience analysis, the authenticity forms the core of the Asian restaurants experience. As a result of the dimensional valence analysis, the staff has a highly positive valence and the service has a highly negative valence dimension. As a result of lexicon salience analysis based on the SVM estimation, the most salience term was food, while the least salience term was Bosphorus. Bosphorus, delicious, and amazing were determined as the terms with the highest positive valence by lexicon valence analysis, respectively. The results may also be of interest to various gastronomy stakeholders interested in the Asian restaurant experience from a customer perspective.
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