## 1 Introduction
Moreover, it is helpful for hotel managers to understand customer demands for different star hotels in the decision-making of marching into new markets through comparing the difference of attribute performance in different star hotels. However, whether the hotel attribute contributing to high/low customer ratings varies across different star hotels has not been verified. Therefore, this study intends to investigate the following question:  
**Research Question 2 (RQ2). **_Does the hotel attribute contributing to high/low customer ratings vary across different star hotels?_  
To answer the above two questions, it is necessary to analyze the effect of attribute performance on customer satisfaction. Customers' preferences, expectations and perceptions on each hotel attribute are influenced by comprehensive factors, thus driving positive and negative customer evaluations toward the bidirectional (good and poor) performance of hotel attributes [22]. Traditionally, one unit increase in good performance and one unit decrease in poor performance concerning a certain hotel attribute should cause the same change of customer satisfaction, thus the relationship between attribute performance and customer satisfaction is assumed to be linear or symmetric [23]. However, some studies have demonstrated that some attributes provide more satisfaction than dissatisfaction [24, 25, 26]. In other words, hotel attributes can have asymmetric effects on customer satisfaction [24]. The Kano model was proposed by [PERSON] et al. (1984) to identify these non-linear or asymmetric relationships between attribute performance and customer satisfaction. The Kano model is often applied to classify hotel attributes into different categories in terms of customer demands, which is helpful for hotel managers to better understand customer expectations and perceptions [27,28]. Meanwhile, considering the limited hotel resources, it is critical to determine attribute priority to maximize customer satisfaction through service improvement. Many studies have shown that applying a combination of the Kano model and importance-performance analysis (IPA) in customer satisfaction analysis can not only analyze customer requirements toward service attributes, but also determine attribute priority [10; 29; 30; 31; 32; 33]. The IPA is a common and effective technique to formulate improvement strategies according to the importance and performance of the attribute [34]. However, existing studies concerning the Kano-IPA model are mainly based on surveys, and few studies use online reviews as a data source for the Kano-IPA model. There are two main reasons limiting the application of the Kano-IPA model in online reviews. On the one hand, online textual reviews are unstructured and therefore need to be processed before they can be converted into usable structured data. On the other hand, there is a question of how to apply the processed data to the customer satisfaction model to obtain different Kano categories. Considering online reviews serving as promising data source for analyzing and improving hotel services, this study intends to apply feature extraction and natural language processing (NLP) techniques to conduct Kano-IPA model through online reviews. In summary, this study aims to identify the well-performed attributes contributing to high customer ratings and poorly performed attributes causing low customer ratings for different star hotels. For this, firstly, we distinguish between positive and negative reviews for different star hotels according to online ratings. Next, we apply feature extraction and sentiment analysis techniques to explore bidirectional performance of hotel attributes. In particular, a new sentiment lexicon for hospitality domain was built from numerous online reviews using the PolarityRank algorithm. To further understand customers' rating behaviors and demands for hotel service, this study intends to conduct the Kano-IPA model through online reviews for attribute classification and prioritizing.