Lung-Hao Lee, Tzu-Mi Lin, Hsiu-Min Shih, Kuo-Kai Shyu, Anna S. Hsu, and Peih-Ying Lu.
In Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025), pages 375-380.
Abstract
This paper describes the ROCLING-2025 shared task aimed at Chinese dimensional sentiment analysis for medical self-refection texts, including task organization, data preparation, performance metrics, and evaluation results. A total of six participating teams submitted results for techniques developed for valence-arousal intensity prediction. All datasets with gold standards and evaluation scripts used in this shared task are publicly available online for further research.
