RT-DIFTWD: A novel data-driven intuitionistic fuzzy three-way decision model with regret theory

Abstract

With the rapid advancement of Web 3.0 and the digital transformation of the tourism industry, online reviews have emerged as a new source of information for potential tourists, making data-driven multi-attribute decision-making feasible. However, the vast number of online reviews significantly increases the complexity of tourists' decision-making. Recognizing the gaps in the current literature, particularly the lack of consideration of tourists' psychological behaviours in their decision-making processes and the inadequate handling of ambiguity and uncertainty in reviews, this study proposes a data-driven intuitionistic fuzzy regret-based three-way decision model (RT-DIFTWD). Specifically, after online reviews are crawled, a satisfaction function based on absolute and relative rationality scenarios with intuitionistic fuzzy sets is established by combining sentiment analysis and regret theory. Moreover, two attribute weight calculation methods that are based on frequency and importance conditions are proposed. A flexible three-way multi-attribute decision-making framework that is suitable for different MADM methods is subsequently proposed for deducing the prioritization and classification of alternatives. Finally, we demonstrate our proposed method through a real application of tourism selection in the Chengdu–Chongqing region. The stability, effectiveness and superiority of the presented method are validated by corresponding experimental studies and a comparative analysis.