@inproceedings{lin-etal-2025-hey,
    title = "Hey Vergil at {ROCLING}-2025 Shared Task: Emotion-Space-Based System for Doctors' Self-Reflection Sentiment Analysis",
    author = "Lin, Ting-Yi  and
      Lin, Cong-Ying  and
      Yeh, Jui-Feng",
    editor = "Chang, Kai-Wei  and
      Lu, Ke-Han  and
      Yang, Chih-Kai  and
      Tam, Zhi-Rui  and
      Chang, Wen-Yu  and
      Wang, Chung-Che",
    booktitle = "Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)",
    month = nov,
    year = "2025",
    address = "National Taiwan University, Taipei City, Taiwan",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/dashboard/2025.rocling-main.45/",
    pages = "407--412",
    ISBN = "979-8-89176-379-1",
    abstract = "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."
}