Hotel selection method based on online evaluations has become a hot research topic. The existing models based on online ratings or reviews from one website have a disadvantage of information being definite and information amount being small. Therefore, this paper proposes a hotel selection model based on Probabilistic linguistic Term Set (PLTS) which integrates online ratings and reviews from multiple websites: (1) Unifying the rating information’s evaluation attributes among different websites based on the PLTS similarity calculation method, putting forward the transformation method of linguistic scale to unify the rating information’s evaluation scale among different websites; (2) Analyzing the sentiment of review texts and putting forward the aggregation model of user reviews based on different groups' risk attitudes; (3) Improving the linguistic scale function to introduce the unbalanced effect of positive and negative evaluations; (4) According to preference differences among different groups, putting forward the attribute weight calculation method and providing recommendation results for different groups. Take four hotels on TripAdvisor, Ctrip and Hostelworld websites for case studies. The results show that information can be used to a greater extent by integrating online ratings and reviews from multiple websites, thus providing consumers with more objective and reliable decision-making results.

基于在线评价的酒店选择方法成为研究热点。现有的基于一个网站的在线评分或评论的模型存在信息明确、信息量少的缺点。因此，本文提出了一种基于概率语言术语集（PLTS）的酒店选择模型，该模型集成了多个网站的在线评分和评论：（1）基于PLTS相似度计算方法统一不同网站之间的评分信息的评价属性，提出统一不同网站间评分信息评价量表的语言量表转换方法；(2) 分析评论文本的情绪，提出基于不同群体风险态度的用户评论聚合模型；(3) 改进语言量表函数，引入正负评价的不平衡效应；(4)根据不同群体之间的偏好差异，提出属性权重计算方法，为不同群体提供推荐结果。以 TripAdvisor、携程和 Hostelworld 网站上的四家酒店为例进行案例研究。结果表明，通过整合来自多个网站的在线评分和评论，可以更大程度地利用信息，从而为消费者提供更加客观可靠的决策结果。提出属性权重计算方法，为不同的群体提供推荐结果。以 TripAdvisor、携程和 Hostelworld 网站上的四家酒店为例进行案例研究。结果表明，通过整合来自多个网站的在线评分和评论，可以更大程度地利用信息，从而为消费者提供更加客观可靠的决策结果。提出属性权重计算方法，为不同的群体提供推荐结果。以 TripAdvisor、携程和 Hostelworld 网站上的四家酒店为例进行案例研究。结果表明，通过整合来自多个网站的在线评分和评论，可以更大程度地利用信息，从而为消费者提供更加客观可靠的决策结果。