## 1 Introduction  
Unlike using recommendations of relatives and friends in the past, people increasingly make hotel booking decisions relying on online reviews on various online travel platforms in the modern era. Hotel online reviews are posted by numerous customers according to their experiences in hotels, which are perceived as more objective, trustworthy and helpful than information provided by hotels [1, 2]. Online reviews generally consist of online ratings and textual reviews. Online ratings signal customer satisfaction or dissatisfaction with hotels. Textual reviews contain customers' actual expectations, feelings and perceptions about hotel services. According to bounded rationality model, customers are unable to elaborate and extract useful information from numerous and heterogeneous data, thus driving them to prefer and rely more on ratings than on textual reviews [3]. As more and more potential customers regard the online ratings as one of the direct references of hotel quality when selecting hotels, it is crucial for hotels to obtain high customer ratings to achieve the goal of improving online bookings [4, 5]. Therefore, exploring what contributes to the difference in online ratings between satisfied and dissatisfied customers is particularly important for hotels. In other words, for the purpose of being competitive sustainably in the hospitality industry, it is critical for hotels to understand the determinants of customer satisfaction and dissatisfaction which are proxied by online ratings [6,7]. Existing studies have proved that the performance of multiple hotel attributes is strongly correlated with customer satisfaction [8, 9, 10]. Most studies have investigated the hotel attributes that lead to customer satisfaction and dissatisfaction through surveys [11, 12, 13]. Recently, with the development of data mining techniques, online reviews serve as the promising data source for customer satisfaction analysis. Several scholars have analyzed the attribute performance through online reviews using sentiment analysis methods, and hence found the determinants of customer satisfaction in the hotel industry [14,15]. However, these studies processed hotel reviews as a whole dataset, neglecting discriminating positive and negative reviews. Processing hotel reviews as a whole can compare the overall performance of multiple attributes from the perspective of all customers but could not distinguish between contributors of customer satisfaction and factors resulting in customer dissatisfaction. Previous studies have found that dual-valence (that is, featuring both positive and negative sentiment) reviews existing in hotels of one-five-star ratings [16,17]. The presence of negative sentiment toward attributes in positive reviews and positive sentiment toward attributes in negative reviews was observed [18, 19, 20]. In other words, even if the performance of several hotel attributes does not meet customer expectations, customers are still satisfied with the hotel and give high ratings to the hotel because of the good performance of other hotel attributes. Meanwhile, customers can be very dissatisfied with the hotel and give low ratings to hotels when the performance of some certain hotel attributes is poor, even though they think other hotel attributes perform well. Therefore, it's necessary to investigate the following question:  
**Research Question 1 (RQ1). **_Which hotel attribute with good performance contributes to high customer ratings and which hotel attribute with poor performance causes low customer ratings?_  
In fact, it should be pointed out that customers' expectations and perceptions vary across different market segments, such as different hotel star ratings [14,21]. Exploring the determinants of customer satisfaction and dissatisfaction of each market segment is beneficial for making more appropriate and precise strategies [10].