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人机有效交互的基础。本文提出情感碰撞器（EC-Net），一种用于多模态情感建模的双曲超图框架。EC-Net利用庞加莱球嵌入表示模态层次结构，并通过在节点与超边之间双向传递消息的超图机制进行融合。为增强类别区分度，对比学习在双曲空间中构建，并采用解耦的径向与角度优化目标。通过自适应超边构建，模型保留了跨时间步与跨模态的高阶语义关系。在多模态情感标准基准测试上的实验结果表明，EC-Net能够生成鲁棒且语义连贯的表征，并持续提升分类准确率，尤其在模态部分缺失或受噪声干扰时表现突出。这些发现表明，显式的层次几何结构与超图融合机制相结合，能够有效实现鲁棒的多模态情感理解。