Emotional Earth Mover’s Distance for Fine-Grained Hierarchical Emotion Analysis
Published in International Conference on Advanced Data Mining and Applications (ADMA 2025), 2025
Recommended citation: Yu H T, Li D, Kang X. Emotional Earth Mover's Distance for Fine-Grained Hierarchical Emotion Analysis[C]//International Conference on Advanced Data Mining and Applications. Singapore: Springer Nature Singapore, 2025: 296-310. https://link.springer.com/chapter/10.1007/978-981-95-3453-1_20
Effective emotion understanding has become increasingly important in developing human-centered interactive systems, such as chatbots and companion/service robots. Despite the remarkable successes made by previous studies, we found that the hierarchical information among emotion labels has been ignored when either quantifying the optimization objective or evaluating the performance, hindering the achievement of fine-grained hierarchical emotion analysis.
To bridge this gap, we propose Emotional Earth Mover’s Distance (EEMD), a novel framework that extends the Earth Mover’s Distance (EMD) to emotion analysis by explicitly encoding the hierarchical structure of emotion labels. This hierarchical distance is integrated into both the training loss and evaluation metric, allowing EEMD to effectively capture the hierarchical emotion nuances throughout the entire cycle of emotion analysis.
Source Code: GitHub Repository
Recommended citation: Yu H T, Li D, Kang X. Emotional Earth Mover’s Distance for Fine-Grained Hierarchical Emotion Analysis[C]//International Conference on Advanced Data Mining and Applications. Singapore: Springer Nature Singapore, 2025: 296-310.