## 2 Related Literature  
### Online Customer Reviews Analysis  
Recently, Online Travel Agencies (OTAs) have emerged, and customers can search, compare prices, and make online reservations through various platforms. Customer reviews thus have been increasingly important sources providing informative data for customers when making purchasing decisions. Three directions can be categorized in the literature of online customer reviews. The first category involves using statistical analysis or hypothesis testing. [PERSON] and [PERSON] [3] employed correlation analysis to investigate review text and sentiments. The study provided insights regarding the reviews' impact on potential customers. [PERSON] et al. [4] used the number of reviews and review valence to analyze occupancy and revenue per available room. This study revealed that review volume and review valence influenced room occupancy and revenue per available room, respectively. Furthermore, hotel locations and room quality are the two most essential terms of review comments. [PERSON] et al. [5] used statistical methods to analyze the impact of online customer reviews on financial profitability. It was reported that positive comments were significantly related to hotel financial performance. [PERSON] et al. [6] employed customers' reviews to analyze distributions of domestic and international customers' hotel ratings. This study pointed out that domestic travel, international travel, language, and culture had a statistically significant impact on hotel preference and satisfaction. The second category involves employing data-driven approaches to explore the relationship between customers' reviews and hotel performance. [PERSON] et al. [7] used a cross-industry standard process for data mining to investigate the influences of online reviewers' comments on restaurant performance. [PERSON] et al. [8] utilized a partial least squares path method to examine the influences of positive and negative reviews on revenue per available room. This study concluded that \"stars\" and percentages of positive comments had a critical impact on revenue per available room. The third category uses sentiment analysis tools, such as a sentiment dictionary, to understand online customer reviews and the impact of reviews. [PERSON] et al. [9] indicated that the users' opinions were usually unstructured, and users were used to expressing opinions in their own ways. This study employed fuzzy ontology to homogenize information; therefore, subjective and unformatted information could be handled. [PERSON] et al. [10] used standardized coefficients to analyze the interrelationship between ratings, sentiments, and sales rankings. The study reported that ratings did not have a significant impact on sales directly but did have an indirect impact through sentiments. [PERSON] et al. [11] utilized a majority decision algorithm and a stochastic gradient descent technique to classify sentimental orientations of WeChat texts. The numerical results indicated that the presented method outperformed the other four machine learning approaches.  
In the previous three categories of studies mentioned, descriptive statistics, hypothesis testing, correlation analysis, cluster analysis, and regression analysis were employed by studies to analyze the influence of customers' online reviews on hotel performance. Some investigations focused on exploring the relationship between emotional words and ratings. However, predictions of hotel performance, such as room occupancy, have not been conducted.