A fuzzy decision support model for online review-driven hotel selection by considering risk attitudes of customers
针对在线评论信息模糊且量大导致酒店选择困难的问题，从顾客风险态度出发，提出一个基于在线评论的模糊决策支持模型，通过属性提取、情感分析和改进的模糊方法帮助顾客选出最优酒店。
Online hotel reviews provide a vital information source for customers to select an optimal hotel, but a large amount of vague and unstructured information increases the difficulty of decision-making. From the perspective of customers’ risk attitudes, this paper proposes a novel fuzzy decision support model for hotel selection based on online reviews. Firstly, the useful information from online reviews is extracted by attribute extraction and sentiment analysis, and then this information is aggregated into the Probabilistic Linguistic Term Set (PLTS) by considering the weight of each review. Secondly, the improved linguistic scale functions are constructed from the perspective of customers’ risk attitude to convert PLTS into quantitative information. Thirdly, an integrated attribute weighting method is presented based on objective weights of the statistical measure and subjective weights of the Analytic Hierarchy Process (AHP) technique. Fourthly, an extended Combinative Distance-based Assessment (CODAS) method is developed to evaluate the performances of hotels. The effectiveness of the proposed model is verified by the practical case from TripAdvisor.com and the comparative analysis with the existing methods.