@inproceedings{chan-etal-2025-kolab,
    title = "{KOL}ab at {ROCLING}-2025 Shared Task: Research on Emotional Dimensions in {C}hinese Medical Self-Reflection Texts",
    author = "Chan, Chia-Yu  and
      Wang, Chia-Wen  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.46/",
    pages = "413--417",
    ISBN = "979-8-89176-379-1",
    abstract = "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."
}