Sentiment and Emotion-aware Multi-criteria Fuzzy Group Decision Making System

2024-08-21

Fuzzy Systems and Data Mining

AI Summary

Traditional group decision-making (GDM) relies heavily on explicit numerical preferences and struggles to handle the abundance of unstructured textual opinions prevalent in real-world scenarios. To address this, this paper proposes a novel multi-criteria fuzzy GDM framework that integrates fine-grained sentiment and emotion-aware analysis. Leveraging natural language processing (NLP), the method automatically extracts sentiment polarity, emotion categories, and implicit preferences from user reviews and other textual inputs, and deeply embeds these features into fuzzy inference and multi-criteria decision processes—enabling semantic quantification of textual opinions and consensus-driven modeling. Its key innovation lies in the first systematic incorporation of emotion dimensions into fuzzy GDM, thereby compensating for the semantic limitations of purely numeric inputs. Evaluated on a real-world hotel selection task involving friend groups, the approach significantly improves group consensus, empirically validating the effectiveness of emotion awareness in enhancing consensus achievement.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Machine Learning: Learning Preferences or Rankings
• Natural Language Processing: Discourse, Pragmatics & Argument Mining

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

In today's world, making decisions as a group is common, whether choosing a restaurant or deciding on a holiday destination. Group decision-making (GDM) systems play a crucial role by facilitating consensus among participants with diverse preferences. Discussions are one of the main tools people use to make decisions. When people discuss alternatives, they use natural language to express their opinions. Traditional GDM systems generally require participants to provide explicit opinion values to the system. However, in real-life scenarios, participants often express their opinions through some text (e.g., in comments, social media, messengers, etc.). This paper introduces a sentiment and emotion-aware multi-criteria fuzzy GDM system designed to enhance consensus-reaching effectiveness in group settings. This system incorporates natural language processing to analyze sentiments and emotions expressed in textual data, enabling an understanding of participant opinions besides the explicit numerical preference inputs. Once all the experts have provided their preferences for the alternatives, the individual preferences are aggregated into a single collective preference matrix. This matrix represents the collective expert opinion regarding the other options. Then, sentiments, emotions, and preference scores are inputted into a fuzzy inference system to get the overall score. The proposed system was used for a small decision-making process - choosing the hotel for a vacation by a group of friends. Our findings demonstrate that integrating sentiment and emotion analysis into GDM systems allows everyone's feelings and opinions to be considered during discussions and significantly improves consensus among participants.

Problem

• Extracting opinions from natural language text in group decisions
• Integrating sentiment and emotion analysis into multi-criteria decision making
• Improving consensus through fuzzy inference with emotional preferences

Innovation

• Uses NLP to analyze sentiment and emotion
• Integrates fuzzy inference for preference scoring
• Combines text analysis with numerical inputs