A Text Mining and Multidimensional Sentiment Analysis of Online Restaurant Reviews |
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| Authors: | Qiwei Gan Yang Yu Lei Jin |
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| Institution: | 1. Department of Business and Economics, School of Business, Edinboro University, Edinboro, Pennsylvania, USA;2. Management Information Systems, Saunders College of Business, Rochester Institute of Technology, Rochester, New York, USA.;3. Department of Information Technology, College of Business, San Francisco State University, San Francisco, California, USA |
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| Abstract: | This study aims to identify the structure of online restaurant reviews and examine the influence of review attributes and sentiments on restaurant star ratings. While past research indicated four attributes specific to restaurant reviews—food, service, ambience, and price—this study proposes context as the fifth attribute unique to online reviews. Sentiment analysis of online restaurant reviews has confirmed the proposed underlying structure of online restaurant reviews. Results showed that consumers’ sentiments in these five attributes significantly explained the differences in star ratings. Food, service, and context are the top three attributes affecting star ratings, followed by price and ambiance. |
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| Keywords: | Attributes context online restaurant reviews sentiment analysis text mining |
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