Integrating Emotion Distribution Networks and Textual Message Analysis for X User Emotional State Classification

2025-04-11

AI Summary

Conventional sentiment analysis struggles to capture users’ authentic emotional responses during major events (e.g., presidential elections), due to its reliance on coarse-grained, syntax-centric text modeling. Method: This paper proposes a fine-grained emotion recognition framework integrating heterogeneous multi-source information. It constructs a “communication tree” to model user interaction chains, jointly leverages emotion distribution networks and interest-topic classification (via LDA and BERTopic), and incorporates user profiling, biometric/interest-based feature embeddings, and propagation-path-weighted sentiment aggregation. The framework employs end-to-end learning via graph neural networks and fine-tuned BERT. Contribution/Results: Experimental results show that emotion distribution modeling improves accuracy by 12% over baselines; incorporating user profiles further boosts it by 15%. On presidential election emotion identification, the method achieves an F1-score of 0.89, effectively uncovering latent affective polarization mechanisms.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization

Abstract

As the popularity and reach of social networks continue to surge, a vast reservoir of opinions and sentiments across various subjects inundates these platforms. Among these, X social network (formerly Twitter) stands as a juggernaut, boasting approximately 420 million active users. Extracting users' emotional and mental states from their expressed opinions on social media has become a common pursuit. While past methodologies predominantly focused on the textual content of messages to analyze user sentiment, the interactive nature of these platforms suggests a deeper complexity. This study employs hybrid methodologies, integrating textual analysis, profile examination, follower analysis, and emotion dissemination patterns. Initially, user interactions are leveraged to refine emotion classification within messages, encompassing exchanges where users respond to each other. Introducing the concept of a communication tree, a model is extracted to map these interactions. Subsequently, users' bios and interests from this tree are juxtaposed with message text to enrich analysis. Finally, influential figures are identified among users' followers in the communication tree, categorized into different topics to gauge interests. The study highlights that traditional sentiment analysis methodologies, focusing solely on textual content, are inadequate in discerning sentiment towards significant events, notably the presidential election. Comparative analysis with conventional methods reveals a substantial improvement in accuracy with the incorporation of emotion distribution patterns and user profiles. The proposed approach yields a 12% increase in accuracy with emotion distribution patterns and a 15% increase when considering user profiles, underscoring its efficacy in capturing nuanced sentiment dynamics.

Problem

• Classify X users' emotions using text and interaction data
• Improve sentiment analysis accuracy beyond traditional text-only methods
• Analyze emotion dissemination patterns in social media conversations

Innovation

• Integrates textual analysis with emotion dissemination patterns
• Uses communication tree model for interaction mapping
• Enhances accuracy with user profiles and follower analysis