Human beings make a variety of decisions daily, ranging from where to invest their money to how to improve their businesses. Decision-support systems are valuable tools that assist in complex decisions, frequently involving input from multiple experts. Nevertheless, Large-Scale Group Decision-Making methods are often utilized when dealing with many experts. Nonetheless, such methods bring about several challenges. The first is managing the vast amount of information generated during the process, given the larger number of participants. The second is controlling the tension that can arise during debates. The third is managing relationships among the experts, given the potential for controversy and argument. To tackle these issues, this paper introduces a novel Large-Scale Group Decision-Making process that applies a multi-criteria approach and a multi-granular modelling technique, which leverages sentiment analysis to optimize the consensus value. The proposed method was validated through an illustrative example with 20 experts and four alternatives. The process achieved a consensus level of 0.8915, exceeding the predefined threshold, and the final ranking of alternatives was ⟨x 3 , x 2 , x 4 , x 1 ⟩, with x 3 identified as the most preferred option. These results confirm the effectiveness of our approach in enhancing consensus and ensuring fair decision outcomes. With this method, experts have the freedom to express themselves as they wish, and the debate gains greater relevance as additional information is extracted and employed.
Trillo et al. (Fri,) studied this question.
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