A novel TODIM-based multi-attribute decision making method under information described by Z-numbers for selecting online B&B

Abstract

This study designs a new TODIM-based multi-attribute decision making (MADM) method under information described by Z-numbers for selecting online Bed and Breakfast (B&B), which includes extracting the evaluation attributes and obtaining the best online B&B. To do that, first the fine-tuned Bidirectional Encoder Representation from Transformers (BERT) model is used to analyze the sentiment of all online reviews from nine regions of Airbnb, which these reviews about online B&Bs are divided into positive and negative comments. And then Term Frequency-Inverse Document Frequency (TF-IDF) is used to extract attribute feature words of positive and negative comments. Their intersection is sought to determine the attributes of the tourists' online B&B selection. Next, the ranking technique of Z-number and the distance of Z-number are put forward. Finally, a novel TODIM-based MADM method under information described by Z-numbers is proposed to select the B&Bs by the online review of B&Bs obtained from Ctrip.com. The advantage of this method is that it not only considers the reliability of online reviews, but also reflects the psychological factors of tourists. The feasibility of the proposed method is illustrated by ranking the B&Bs, and the flexibility and superiority of the method are highlighted by the sensitivity analysis and comparative analysis.