###### Abstract  
For hotel management, occupancy is a crucial indicator. Online reviews from customers have gradually become the main reference for customers to evaluate accommodation choices. Thus, this study employed online customer rating scores and review text provided by booking systems to forecast monthly hotel occupancy using long short-term memory networks (LSTMs). Online customer reviews of hotels in Taiwan in various languages were gathered, and Google's natural language application programming interface was used to convert online customer reviews into sentiment scores. Five other forecasting models--back propagation neural networks (BPNN), general regression neural networks (GRNN), least square support vector regression (LSSVR), random forest (RF), and gaussian process regression (GPR)--were employed to predict hotel occupancy using the same datasets. The numerical data indicated that the long short-term memory network model outperformed the other five models in terms of forecasting accuracy. Integrating hotel online customer review sentiment scores and customer rating scores can lead to more accurate results than using unique scores individually. The novelty and applicability of this study is the application of deep learning techniques in forecasting room occupancy rates in multilingual comment scenarios with data gathered from review text and customers' rating scores. This study reveals that using long short-term memory networks with sentiment analysis of review text and customers' rating scores is a feasible and promising alternative in forecasting hotel room occupancy.  
s 2021A variety of factors influence hotel operating performance. In the past, historical sales data have been used to analyze demand patterns and trends in addition to hotel factors, such as geographic locations, hotel facilities, service quality, and marketing activities. Recently, with the rise of online booking platforms, customers' behavior patterns in searching and choosing hotel accommodations have changed. When online customers choose a hotel, they target the date and area. Then, they filter out hotel information that meets their needs on the basis of the accommodation options provided by the online travel agent booking website. Finally, when determining accommodation, previous customer reviews tend to be an influential factor. Thus, more studies are using customer online comment analysis as the subject of hotel management research. The Tourism Bureau of the Ministry of Transportation in Taiwan pointed out some statistical indicators ranking the operating performance of hotels, such as total revenue, annual occupancy rate, annual average price, and the annual average price of saleable rooms. The revenue of tourist hotels usually comes from the guest room and catering departments. However, the consumer reviews on the booking websites are mainly for guests' consuming experience of hotel accommodation. Thus, hotels' total operating incomes are not appropriate for further analysis. Furthermore, differences in total operating income and average house prices may be caused by the number of operating rooms, locations, and decoration equipment. Therefore, this study employed hotel room occupancy rates as a criterion for evaluating hotel operating performance. The rest of this study is organized as follows: Section 2 discusses the literature, Section 3 briefs long short-term memory networks, Section 4 introduces the proposed forecasting architecture for hotel room occupancy, Section 5 depicts the numerical results, and Section 6 presents the conclusions.