Fuzzy decision making for cross domain sentiment analysis and MABAC approach with complex T-spherical fuzzy operators

Cross-domain sentiment analysis is an advanced approach in natural language processing that focuses on transferring sentiment knowledge from one domain to another, where labeled data may be scarce or vague. To overcome the impact of redundant and ambiguous information about the sentiment expressions, we explore a potent approach of a fuzzy framework with robust algorithms of a decision analysis system. This article initiates an advanced decision-making approach of the multi-attributive border approximation area comparison (MABAC) method to aggregate human judgments or opinions precisely and accurately. To achieve the goals of the presentation, we expose the notion of complex t-spherical fuzzy set (CT-SFS), which is used to manage uncertainty and vagueness during the aggregation process. Some feasible operations of Sugeno-Weber t-norm and t-conorm are formulated under the system of complex t-spherical fuzzy information. A family of Sugeno-Weber weighted average and weighted geometric operators is also developed based on a complex t-spherical fuzzy framework. To showcase the validation and strength of the discussed mathematical models, an intelligent decision algorithm of the MABAC method is established to resolve multi-criteria decision-making (MCDM) problems under complex t-spherical fuzzy environments. An experimental case study is discussed to evaluate different sentiment recognition methods under conflicting criteria and aggregation operators. Additionally, a comparative analysis is conducted to highlight the superiority and effectiveness of the diagnosed mathematical terminologies.