Busemann energy-based attention for emotion analysis in Poincaré discs
We present EmBolic - a novel fully hyperbolic deep learning architecture for fine-grained emotion analysis from textual messages. The underlying idea is that hyperbolic geometry efficiently captures hierarchies between both words and emotions. In our context, these hierarchical relationships arise from semantic ambiguities. EmBolic aims to infer the curvature on the continuous space of emotions, rather than treating them as a categorical set without any metric structure. In the heart of our architecture is the attention mechanism in the hyperbolic disc. The model is trained to generate queries (points in the hyperbolic disc) from textual messages, while keys (points at the boundary) emerg...
| Authors | Zinaid Kapić, Vladimir Jaćimović |
|---|---|
| Primary category | cs.LG |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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