Message Passing on Semantic-Anchor-Graphs for Fine-grained Emotion Representation Learning and Classification

Emotion classification has wide applications in education, robotics, virtual reality, etc. However, identifying subtle differences between fine-grained emotion categories remains challenging. Current methods typically aggregate numerous token embeddings of a sentence into a single vector, which, while being an efficient compressor, may not fully capture their complex semantic and temporal distributions. To solve this problem, we propose SEmantic ANchor Graph Neural Networks (SEAN-GNN) for fine-grained emotion classification. It learns a group of representative, multi-faceted semantic anchors in the token embedding space: using these anchors as global reference, any sentence can be projected onto them to form a "semantic-anchor graph", with node attributes and edge weights quantifying semantic and temporal information, respectively. The graph structure is well aligned across sentences and, importantly, allows for generating comprehensive emotion representations regarding K different anchors. Message passing on the anchor graph can further integrate the semantic and temporal information and refine the learned features. Empirically, SEAN-GNN produces meaningful semantic anchors and discriminative graph patterns, with promising classification results on 6 popular benchmark datasets against state-of-the-arts.