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.

翻译：情感表达是自然交流与高效人机交互的基础。本文提出情感碰撞器（EC-Net），一种用于多模态情感与情绪建模的双曲超图框架。EC-Net利用庞加莱球嵌入表征模态层次结构，并通过超图机制实现融合，该机制在节点与超边之间双向传递信息。为增强类别分离度，我们在双曲空间中引入对比学习，并解耦径向与角度目标函数。通过自适应超边构建，保留跨时间步与模态的高阶语义关系。在标准多模态情感基准上的实验结果表明，EC-Net生成鲁棒且语义一致的表征，尤其在模态部分缺失或受噪声干扰时，能持续提升准确率。这些发现表明，显式层次几何与超图融合的结合对于鲁棒性多模态情感理解是有效的。