Study On The Hotel Customer Satisfaction Evaluation System

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.At the same time,more and more research and analysis focus on such huge amounts of data to obtain accurate and efficient information and bringing new value for the operation and management services of enterprises.In China's hotel industry,the number of high-star hotels is increasing rapidly,and the product structure in some regions is not balanced,leading to fierce market competition.At the same time,due to the novice trend of diversification of accommodation formats,the emergence of various accommodation products has a greater impact on the traditional high-star hotels in China,so that hotels continue to face a fierce market competition environment.If high-star hotels want to develop in the long run,they need to face customer demand more accurately and improve customer satisfaction.Therefore,a more scientific and effective customer satisfaction evaluation system is an important problem faced by hotels.Research shows that the big data of online review can help us understand the focus and needs of customers more accurately,and build a customer satisfaction evaluation system based on this basis.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.And twenty-eight high-frequency word classification factors and six division dimensions of Hangzhou high-star hotels are reduced.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.Through the Bayesian network to establish the relationship between each dimension and the corresponding factors,and obtain the conditional probability table that the secondary factor affects the customer satisfaction when the first-level indicator dimension affects the customer satisfaction,to determine the final Hangzhou high-star hotel customer satisfaction evaluation system.Finally,taking a high-star hotel in Hangzhou as an example to carry out the application and verification of the evaluation system.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.