Emotion Collider: Dual Hyperbolic Mirror Manifolds for Sentiment Recovery via Anti Emotion Reflection

• URL: http://arxiv.org/abs/2602.16161v1
• Date: Wed, 18 Feb 2026 03:19:05 GMT
• 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,
• Abstract summary: Emotion Collider (EC-Net) is a hyperbolic hypergraph framework for multimodal emotion and sentiment modeling.<n>EC-Net represents hierarchies using Poincare-ball embeddings and performs fusion through a hypergraph mechanism.<n> Empirical results show that EC-Net produces robust, semantically coherent representations and consistently improves accuracy.
• Score: 19.83275015213163
• License: http://creativecommons.org/licenses/by/4.0/
• 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 resilient multimodal affect understanding.

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