Travelers can enjoy a wide range of choices with the assistance of online review websites such as TripAdvisor. Online reviews provided by customers are an important portion of hotels' online business worldwide as they have value in terms of understanding customers' observations of hotels' product and service features. Hotel managers seek to understand travelers' satisfaction and hotel preferences through online reviews to improve their marketing strategy and decision making. This research uses the travelers' generated content in online hotel reviews to provide reasonable and benchmarking understandings about customers' satisfaction and preferences. Hence, the aim of this study is identifying the important factors for hotel selection based on previous travelers' reviews on TripAdvisor. Accordingly, we develop a new method for the use of Multi-Criteria Decision-Making (MCDM) and soft computing approaches. Concentrating on the case study of the Canary Islands hotels, we show how this method can be applied to determine the satisfaction and preferences among travelers that impact their decision in hotel choices. The results help to identify four customer segments for Canary Islands hotels. These segments are “Highly Satisfied Travelers”, “Satisfied Travelers”, “Moderately Satisfied Travelers”, and “Unsatisfied Travelers”, showing that different travelers have various degrees of satisfaction with dissimilar preferences. We found that travelers' preference and satisfaction segmentation is a crucial stage in travelers' behavior analysis to improve the quality of hotels' products and services. This form of analysis can enhance hotel managers' understanding of different market segments according to customers’ satisfaction level and their preferences. The findings of this study will help managers to set priority instructions for improving the corresponding hotel features and use online customer reviews to improve customer satisfaction and hotel performance.