A new Consensus Reaching Method for Group Decision-Making based on the Large Language Model Gemini for Detecting Hostility During the Discussion Process

Group decision-making, an everyday practice ranging from simple choices to long-term investments, is influenced by the inherent ambiguity of the natural language we use as people. The ambiguity in this instance stands in stark contrast to the precision of language employed by machines. Furthermore, expressing emotions, such as aggressiveness or happiness, in natural language can pose a problem by influencing the group decision-making process. To address this challenge, we propose a consensus method for group decision-making that, through sentiment analysis, assesses aggressiveness in comments using generative artificial intelligence, specifically the large language models provided by Google. Using a Gemini API, we compute the degree of hostility in the comments, adjusting the weight of each expert. This ensures that more aggressive experts have less influence on the decision-making process. In such a way, this tool is able not only to encourage more objective language but also to prevent experts from unduly influencing others through their comments.