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.