Hotel selection is an important decision in making travel plans. Since hotel selection is a typical non-expert decision, online reviews provide people with information about the hotels and travel destinations they never went to. Several studies construct the decision model based on online reviews with the subjective weights of criteria but ignore the objective weight of criteria derived by opinion leaders, which contributes to the review helpfulness. This study proposes a decision-making model based on online reviews for satisfactory hotel selection. Firstly, an RFMP model is proposed to extract the online reviews of opinion leaders, and the Word2vec method is used to extract the criteria from the online review of opinion leaders. Secondly, obtain the objective weight of criteria from the online reviews of opinion leaders by Word2Vec. Meanwhile, obtain the subjective weight of criteria by the best worst method(BWM) method. Thus, the weight of the hotel selection criteria can be obtained by a linear weighting of objective and subjective weight with a parameter. Thirdly, the hotel selection process based on TOPSIS is employed. Finally, a case study of 8 alternative hotels on Mafengwo.com is applied to verify the proposed model. Comparison experiments and sensitivity analysis are given to illustrate the reasonableness and advantage of the proposed model.

This article proposes a comprehensive star rating approach for cruise ships by the combination of subject and objective evaluation. To do that, it firstly established a index system of star rating for cruise ships. Then, the modified TOPSIS is adopted to tackle objective data for obtaining star ratings for basic cruise indicators and service capabilities of cruise ships. Thus, the concept of distributed linguistic star rating function (DLSRF) is defined to analyze the subjective evaluation from experts and users. Hence, a novel weight calculation method with interactive group decision making is presented to assign the importance of the main indicators. Particularly, in order to enable decision makers to effectively deal with the uncertainty in this star rating process, it adopts the personalized individual semantics (PIS) model. Finally, data of nine cruise ships is collected to obtain their final star rating results and some suggestions for improving cruise service capabilities and star indicators were put forward.