Integrating Sentiment Analysis and Intuitionistic Fuzzy Set for Large-Scale Decision-Making

Traditional group decision-making methods are typically employed with a limited number of experts and alternatives. Nevertheless, in real-world scenarios, such as those involving social networks or e-democracy in municipalities or companies, these methods face limitations due to the involvement of many experts and the vast amount of generated information. In addressing this challenge, Large-Scale Group Decision-Making methods have been developed. Nonetheless, enhancements remain needed to leverage the wealth of available information fully. For example, expert comments during debates are often overlooked. We have devised an innovative approach to tackle this issue and effectively manage the information provided by experts. Our method facilitates expert debates where preferences, both positive and negative, are expressed, and leverages this input to derive neutral information through intuitionistic fuzzy sets, thereby enhancing the decision-making process. Furthermore, employing sentiment analysis, our method categorizes comments as positive, negative, or neutral. By integrating expert insights, sentiment analysis results, and a newly developed aggregation operator, we consolidate information, enabling the calculation of a comprehensive ranking that accounts for the abundance of available data.