Semantic alignment in hyperbolic space for fine-grained emotion classification

Existing approaches to fine-grained emotion classification (FEC) often operate in Euclidean space, where the flat geometry limits the ability to distinguish semantically similar emotion labels (e.g., annoyed vs. angry).While prior research has explored hyperbolic geometry to capture fine-grained label distinctions, it typically relies on predefined hierarchies and ignores semantically similar negative labels that can mislead the model into making incorrect predictions.In this work, we propose HyCoEM (Hyperbolic Contrastive Learning for Emotion Classification), a semantic alignment framework that leverages the Lorentz model of hyperbolic space.Our approach embeds text and label representations into hyperbolic space via the exponential map, and employs a contrastive loss to bring text embeddings closer to their true labels while pushing them away from adaptively selected, semantically similar negatives.This enables the model to learn label embeddings without relying on a predefined hierarchy and better captures subtle distinctions by incorporating information from both positive and challenging negative labels.Experimental results on two benchmark FEC datasets demonstrate the effectiveness of our approach over baseline methods. 1