@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T KOLab at ROCLING-2025 Shared Task: Research on Emotional Dimensions in Chinese Medical Self-Reflection Texts
%A Chan, Chia-Yu
%A Wang, Chia-Wen
%A Yeh, Jui-Feng
%Y Chang, Kai-Wei
%Y Lu, Ke-Han
%Y Yang, Chih-Kai
%Y Tam, Zhi-Rui
%Y Chang, Wen-Yu
%Y Wang, Chung-Che
%S Proceedings of the 37th Conference on Computational Linguistics and Speech Processing (ROCLING 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C National Taiwan University, Taipei City, Taiwan
%@ 979-8-89176-379-1
%F chan-etal-2025-kolab
%X 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.
%U https://aclanthology.org/2025.rocling-main.46/
%P 413-417
Markdown (Informal)
[KOLab at ROCLING-2025 Shared Task: Research on Emotional Dimensions in Chinese Medical Self-Reflection Texts](https://aclanthology.org/2025.rocling-main.46/) (Chan et al., ROCLING 2025)
ACL