In the ROCLING 2025 dimensional sentiment analysis task, we present EmoTracer. It is an emotion-space-based system for analyzing doctors’ self-reflection texts. The system uses XLNet, BERT, and LSTM models. It is trained on the SLAKE medical dataset and Chinese datasets, such as Chinese EmoBank and NRC-VAD. This helps the system capture the possible emotional changes of doctors when they write patient-related reflections. EmoTracer converts texts into Valence and Arousal scores. The experiments show about 60% accuracy, a Pearson correlation coefficient (PCC) of 0.9, and a mean absolute error (MAE) of 0.3. These results can help support mental health management. The system also has a simple front-end UI. Users can enter texts and see the analysis results. This demonstrates the full functionality of the EmoTracer system.