Understanding customer needs is of great significance to enhance service quality and competitive advantage. However, for the tourism industry, it is still unclear how to mine service improvement strategies from tourist-generated online reviews.
Customer satisfaction is a psychological state achieved by customers based on their subjective judgment of the degree to which products or services fulfill their requirements 
. The inherent characteristics of tourism services determine that customer satisfaction is an important reference to evaluate its quality. Firstly, tourism services are experience-oriented services, and the quality of service largely depends on the experience and feelings of tourists themselves, which is difficult to be evaluated by consistent testing standards 
. Secondly, tourists participate in and experience the whole process of the service, and have a tangible perception of its advantages and disadvantages. Thirdly, customer satisfaction has a strong correlation with recommendation intention 
, and improving customer satisfaction is an important driving force for the economic development of tourism enterprises. Some existing studies have shown that effective product improvement or Research and Development analysis can be conducted by modeling or measuring customer satisfaction, supplemented by other relevant models 
. Consumer surveys or experiments are a typical way of measuring customer satisfaction 
. However, this approach requires rigorous design and appropriate procedures to ensure the quality of participants’ responses. Undoubtedly, this is costly in money and time, and the data can become quickly outdated. For the tourism service industry, tourists’ consumption has the characteristics of one-time, mobility and hedonism, which makes it more difficult to carry out customer satisfaction surveys. Therefore, how to use the accumulated online tourism data resources to measure the customer satisfaction of tourism services is worth further research.
User-generated content (UGC) is the public content spontaneously generated by users in the Web2.0 environment, which includes various forms of data such as audio, video, text, pictures, etc. UGC contains a wealth of people’s attitudes, opinions and other information, and the research on its mining is becoming deeper and deeper. Among them, text data have become one of the most important elements reflecting market sentiment, as well as one of the main forms of tourism’s Big Data 
. Online travel reviews objectively reflect the tourists’ real perception of the attractions and services of the tourist destination, which is one of the most important ways of online word-of-mouth communication and is the most authentic portrayal of tourists’ views, feelings, and sentiments. It is also the true expression of tourists, which contains the tourists’ attitude towards the tourism service elements of tourism destinations. Therefore, travel online reviews contain rich information, which is of great value for managers and researchers to understand customer satisfaction 
. In addition, compared with survey research or experimental research, online review data have the characteristics of public availability, low cost, spontaneous generation, great insight, and large number of participants 
, which make online review data more suitable for constructing comprehensive customer satisfaction models. With the above advantages in mind, online travel reviews are a potentially powerful data resource for understanding customer satisfaction with tourism services.
Some studies in the field of tourism have confirmed that customers’ travel sentiments have a significant impact on their satisfaction. For example, a survey on customer satisfaction of diving services shows that high customer satisfaction is closely related to sentiments, such as excitement, pleasure, awe, surprise, etc. All themes in the theoretical framework of diver satisfaction determined in this study are regulated by sentiments 
2. Research on Customer Satisfaction Measurement
With the accumulation of massive online review data, many studies are emerging to analyze customer satisfaction directly or indirectly from online reviews. These studies can be mainly divided into three main streams: (1) to explore the key attributes affecting customer satisfaction from online reviews and conduct sentiment analysis; (2) research on the relationship between product/service attribute performances and customer satisfaction; (3) research on customer satisfaction model based on online reviews.
2.1. Attribute Extraction and Sentiment Analysis Based on Online Reviews
With the rapid development of the Internet, massive UGC has accumulated continuously. UGC contains rich and real customer perspective information and is regarded as a data resource with a strong potential for understanding and managing customer demands 
. However, online review data exist in the form of free text. As a kind of unstructured data, text data cannot be directly analyzed. Therefore, the conversion of text review data into structured data that can be directly utilized is the basis for subsequent analysis. Customer opinion mining from online reviews is one of the most pursued areas of research, which is mainly carried out based on attribute extraction and sentiment analysis 
Attribute extraction refers to extracting topics that users pay frequent attention to from online reviews, as well as keywords related to topics. There are two main categories of attribute extraction methods: (1) The statistical model-based methods, such as association rule mining 
, Hidden Markov Model 
, LDA model 
, etc. Among them, the LDA model becomes one of the most widely used models. For example, the study of Tirunillai and Tellis used the improved LDA model to propose a unified framework for extracting tourism service attributes from online reviews 
. Another study utilized the LDA model to identify the key dimensions of customer satisfaction from 266,554 pieces of online review data 
. (2) The rule-based method, which formulates the corresponding extraction rules according to the characteristics of the review text and the research goal to realize the extraction of attributes. For example, Kang and Zhou 
proposed an unsupervised rule-based approach to identify subjective and objective characteristics from online reviews. Rana and Cheah et al. 
defined a rules-based sequential pattern for online review mining and proposed a rules-based two-stage extraction model for dimensional extraction.
Sentiment analysis can mine the sentiment information hidden in online reviews to help understand customers’ emotional attitudes toward product and service attributes. Sentiment analysis methods are mainly divided into lexical sentiment analysis, such as dictionary-based sentiment analysis and corpus-based sentiment analysis 
and sentiment analysis based on machine learning, such as support vector machine (SVM), Naive Bayes, unsupervised machine learning 
2.2. Research on the Influence of Attribute Performance on Customer Satisfaction
On the basis of attribute and sentiment mining, the subsequent research mainly focuses on analyzing the impact of tourism service attribute performances on customer satisfaction in online reviews. It is worth noting that since online reviews express customers’ views and feelings, attribute performances in related studies refer to customers’ feelings on product/service attributes, which are usually expressed by customers’ emotional attitudes towards attributes. In the field of tourism, the several present studies mainly focus on the hotel industry. For example, a recent study examined how cultural traits affect the role of attribute-level experience on tourist satisfaction, and used a deep learning algorithm to propose an attribute-level sentiment analysis model to extract tourists’ attribute-level experience from online reviews. An empirical study based on nearly 50,000 online reviews collected by TripAdvisor found that positive/negative attribute experience has different impacts on customers with different cultural traits 
. In addition, to understand the customers’ demands of five-star hotels, Bi et al. 
proposed an online review mining method from the perspective of attribute importance–performance analysis (IPA). In this method, LDA is first used to find several useful hotel attributes from online reviews, then SVM is used to analyze customers’ performance feelings on these attributes in the reviews, and then an integrated neural network model is used to calculate the importance of attributes. Finally, an IPA diagram is constructed according to the results to analyze customer demand. From the perspective of service improvement, Zhang et al. 
based on the existing research on the relationship between service performance and customer satisfaction, and considering the influence of consumer expectations and subjective opinions of management, proposed an online review-driven method to determine the priority of hotel service resource allocation. In this method, the LDA model is used to extract service attributes, and the recursive neural tensor network is used to divide the attribute sentiments involved in reviews into five categories. Then, the traditional PRCA model is improved to analyze the asymmetric relationship between attribute performances and customer satisfaction. On this basis, the customer mention frequency is calculated, the customer satisfaction function is constructed, and finally, the improvement strategy analysis is realized under the framework of the Kano model.
These studies have made outstanding contributions to the tourism field to gain insight into customer satisfaction from online reviews. However, there are still limitations in these research methods. First of all, for the research that extracts service attributes from online reviews for performance analysis, there is no quantitative measurement of the impact of these attributes on customer satisfaction, such as the following study 
. Second, even if the influence of service attributes on customer satisfaction is measured, these studies are usually based on the assumption that customer satisfaction (online ratings) follows a Gaussian distribution, and the relationship between satisfaction and all attribute tendencies follows additive independence, such as the following study 
. In fact, in many real situations, customer satisfaction follows a positive skew, asymmetric, bimodal (or J-shaped) distribution 
. At the same time, because the attributes automatically mined from reviews are not as rigorous as those in a well-designed questionnaire, there may be more complex multilinear or nonlinear relationships between different attributes and customer satisfaction. Third, some studies do not pay attention to the categories of service attributes, such as the following study 
. However, some studies have confirmed that service attributes can be divided into different categories, and the attributes of different categories will affect customer satisfaction in different ways 
. For example, performance attributes will cause dissatisfaction when they are not implemented, but satisfaction when they are implemented; however, a higher degree of realization of the reverse attributes will lead to an increase in tourist dissatisfaction. Recently, the empirical study of Xu et al. 
also showed that the attribute type moderates the impact of perceived attribute experience on overall satisfaction. Therefore, identifying the category of tourism service attributes is helpful to provide a clearer improvement direction for promoting tourism satisfaction and realize a more effective allocation of tourism service resources, which is necessary for further research.