Sentiment analysis in restaurants on social media reviews: the case of Giethoorn restaurants - Yu Ting (2020)
Social media has become a main platform for users to express their opinions and feelings and a vast number of available and valuable data in form of text has been created for researchers and operators to hear the users’ voice in different industries. As a consequence, text mining and sentiment analysis have gained big attention and the supporting business intelligence tools to analyze the unstructured data and interpret it into useful and readable information also have been developed rapidly. The Lexalytics, a text mining artificial intelligence tool, is applied to support to present a research method using data mining in order to suggest how to improve the performance of Zwaantje, a restaurant in a touristic Dutch village, through analyzing the reviews of all the restaurants in the village from the most frequently used social media platforms under the four restaurant quality factors namely food and beverage, service, atmosphere and value. Finding of the research is presented by the key themes extracted by Lexalytics with comparison of the customers’ review sentiment between Zwaantje and the benchmark restaurants set by a specific approach under the abovementioned quality dimensions, in which the F&B and service are most commented by the customers. The outcomes demonstrate that text mining can generate insights from different aspects in the restaurant industry and the proposed approach are valuable to the restaurant management.