Currently, most sentiment analysis techniques are primarily applied to general texts such as social media or news reports, and there is still a relative gap in emotion recognition within the medical field. Self reflection involves communication between individuals and their inner selves, which has a positive impact on people’s future lives. This article aims to design a classification model for reflective texts aimed at medical professionals to fill gaps in sentiment analysis within the medical field. This task used a BERT model, trained on a dataset from the Chinese EmoBank, and evaluated using the test set provided by the ROCLING 2025 Dimensional Sentiment Analysis – Shared Task. The assessment results show that Valence and Arousal’s PCC scores are 0.76 and 0.58 respectively, while the MAE scores are 0.53 and 0.82, respectively.