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：群体决策在各种现实场景中扮演着关键角色，在这些场景中，个体通常以自然语言而非结构化数值形式表达意见。传统的群体决策方法往往忽视人类讨论中存在的主观性与模糊性，使得达成公平且基于共识的决策面临挑战。本文提出一种基于模糊共识的群体决策系统，该系统整合情感与情绪分析以从文本输入中提取偏好值。所提出的框架将显式投票偏好与从聊天讨论中推导出的情感分数相结合，随后通过模糊推理系统处理这些数据，以计算每个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，我们引入一种基于模糊逻辑的共识度量模型，该模型通过评估参与者的同意度与置信水平来评估整体反馈。为说明本方法的有效性，我们将该方法应用于餐厅选择场景，其中一组个体必须基于简短的聊天讨论决定用餐选项。结果表明，模糊共识机制能成功聚合个体偏好，并确保产生能准确反映群体情感的平衡结果。