Hotel Selection with User-generated Content Considering Tourists' Preference and Review Helpfulness

Hotel selection is crucial in planning trips. At present, a great number of tourists rely on user-generated content (online text reviews, numerical ratings) to select hotel. Therefore, this study proposed a comprehensive behavioral decision-making approach, which integrates online user-generated text reviews and multi-criteria ratings to help tourists select a hotel considering tourists' preference and review helpfulness. First, review helpfulness is analyzed to improve the quality of data. Second, a decision matrix is constructed by simultaneously considering the sentiment in online helpful reviews and multi-criteria ratings. Third, the criteria whose subjective and objective weights the tourists prefer to consider are derived by the maximizing deviation and best worst methods (BWM), respectively. Finally, to address hotel selection problems, a decision-making method with picture fuzzy information, considering bounded rationality behavior, is developed. Five hotels from the TripAdvisor website were empirically studied for the purpose. The results indicate the reasonableness and advantages of our proposed hybrid approach in helping tourists with more objective and reliable decision making. Concurrently, the findings present implications for theory and for hospitality managers and travel platforms.