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In recent years, with the rapid development and popularization of the mobile Internet, online booking and purchasing of products has become a consumption habit. In order to better meet the needs of consumers, many online booking and purchasing platforms provide consumers with a free and anonymous evaluation model of products. Based on this, a huge amount of product evaluation information has been formed on major booking platforms. This paper uses the review information of Hangzhou high-star hotel in the third-party platform of Tripadvisor.com as the data source of Hangzhou customer satisfaction evaluation system, and using the method text analysis on big data to sort and analyze the collected hotel review information. Then, the Bayesian network model is used to construct the structural learning and parameter learning for each index to determine the factors affecting customer satisfaction. The research shows that the customer satisfaction evaluation system based on the online comment text is more accurate and scientific than the traditional one, which is beneficial to the hotel's accurate evaluation of customer satisfaction and customer demand identification.

Study on the Hotel Customer Satisfaction Evaluation System——Based on Hangzhou High Star Hotel Reviews Data from Tripadvisor.com