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[2602.16161] Emotion Collider: Dual Hyperbolic Mirror Manifolds for Sentiment Recovery via Anti Emotion Reflection
Summary
The paper presents Emotion Collider (EC-Net), a novel framework for multimodal emotion and sentiment modeling using hyperbolic geometry and hypergraph mechanisms to enhance class separation and resilience in emotion understanding.
Why It Matters
This research is significant as it addresses the complexities of emotional expression in human-computer interaction. By utilizing hyperbolic geometry and hypergraph fusion, it offers a robust approach to improve sentiment analysis, especially in noisy or incomplete data scenarios, which is crucial for advancing AI's emotional intelligence.
Key Takeaways
- EC-Net employs hyperbolic hypergraph frameworks for emotion modeling.
- Contrastive learning in hyperbolic space enhances class separation.
- The framework preserves high-order semantic relations across modalities.
- Empirical results show improved accuracy in sentiment analysis.
- Effective for scenarios with partial or noisy data.
Computer Science > Multimedia arXiv:2602.16161 (cs) [Submitted on 18 Feb 2026] Title:Emotion Collider: Dual Hyperbolic Mirror Manifolds for Sentiment Recovery via Anti Emotion Reflection Authors:Rong Fu, Ziming Wang, Shuo Yin, Wenxin Zhang, Haiyun Wei, Kun Liu, Xianda Li, Zeli Su, Simon Fong View a PDF of the paper titled Emotion Collider: Dual Hyperbolic Mirror Manifolds for Sentiment Recovery via Anti Emotion Reflection, by Rong Fu and 8 other authors View PDF HTML (experimental) Abstract:Emotional expression underpins natural communication and effective human-computer interaction. We present Emotion Collider (EC-Net), a hyperbolic hypergraph framework for multimodal emotion and sentiment modeling. EC-Net represents modality hierarchies using Poincare-ball embeddings and performs fusion through a hypergraph mechanism that passes messages bidirectionally between nodes and hyperedges. To sharpen class separation, contrastive learning is formulated in hyperbolic space with decoupled radial and angular objectives. High-order semantic relations across time steps and modalities are preserved via adaptive hyperedge construction. Empirical results on standard multimodal emotion benchmarks show that EC-Net produces robust, semantically coherent representations and consistently improves accuracy, particularly when modalities are partially available or contaminated by noise. These findings indicate that explicit hierarchical geometry combined with hypergraph fusion is effective for...
