Group Decision-Making (GDM) plays a crucial role in various real-life scenarios where individuals express their opinions in natural language rather than structured numerical values. Traditional GDM approaches often overlook the subjectivity and ambiguity present in human discussions, making it challenging to achieve a fair and consensus-driven decision. This paper proposes a fuzzy consensus-based group decision-making system that integrates sentiment and emotion analysis to extract preference values from textual inputs. The proposed framework combines explicit voting preferences with sentiment scores derived from chat discussions, which are then processed using a Fuzzy Inference System (FIS) to compute a total preference score for each alternative and determine the top-ranked option. To ensure fairness in group decision-making, we introduce a fuzzy logic-based consensus measurement model that evaluates participants' agreement and confidence levels to assess overall feedback. To illustrate the effectiveness of our approach, we apply the methodology to a restaurant selection scenario, where a group of individuals must decide on a dining option based on brief chat discussions. The results demonstrate that the fuzzy consensus mechanism successfully aggregates individual preferences and ensures a balanced outcome that accurately reflects group sentiment.

翻译：群体决策（GDM）在众多现实场景中发挥着关键作用，这些场景中个体通常以自然语言而非结构化数值表达意见。传统GDM方法往往忽视人类讨论中存在的主观性与模糊性，导致难以达成公平且基于共识的决策。本文提出一种基于模糊共识的群体决策系统，通过整合情感与情绪分析从文本输入中提取偏好值。该框架将显式投票偏好与聊天讨论衍生的情感分数相结合，继而运用模糊推理系统（FIS）处理数据，计算各备选方案的总偏好分数并确定最优选项。为确保群体决策的公平性，我们引入基于模糊逻辑的共识度量模型，通过评估参与者的同意度与置信水平来量化整体反馈。为验证方法的有效性，我们将该模型应用于餐厅选择场景：一组个体需基于简短聊天讨论决定用餐选择。结果表明，模糊共识机制能有效聚合个体偏好，并确保产生准确反映群体情感倾向的平衡结果。