Group Decision-Making System with Sentiment Analysis of Discussion Chat and Fuzzy Consensus Modeling

2025-03-24

AI Summary

Traditional group decision-making methods struggle to model the subjectivity and fuzziness inherent in natural language opinions, resulting in insufficient fairness and consensus. To address this, we propose a novel framework integrating sentiment analysis with fuzzy consensus modeling. Specifically, we introduce the first approach that jointly feeds fine-grained textual sentiment scores and explicit votes into a fuzzy inference system (FIS), and design a dynamic fuzzy consensus metric capable of evaluating individual confidence levels. Our method automatically extracts subjective preferences from chat transcripts and quantifies group consistency, enabling joint optimization of affective orientation and consensus level. In an empirical restaurant selection study, the system significantly improves decision quality, achieving a 37% increase in consensus attainment rate. This demonstrates the framework’s effectiveness and advancement in real-world, language-mediated group interaction scenarios.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Extracts preferences from text using sentiment analysis
• Measures consensus with fuzzy logic for fair decisions
• Combines chat sentiment and voting for group choices

Innovation

• Integrates sentiment analysis for preference extraction
• Uses Fuzzy Inference System for preference scoring
• Employs fuzzy logic for consensus measurement