@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://aclanthology.org/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."
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%0 Conference Proceedings
%T Hey Vergil at ROCLING-2025 Shared Task: Emotion-Space-Based System for Doctors’ Self-Reflection Sentiment Analysis
%A Lin, Ting-Yi
%A Lin, Cong-Ying
%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 lin-etal-2025-hey
%X 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.
%U https://aclanthology.org/2025.rocling-main.45/
%P 407-412
Markdown (Informal)
[Hey Vergil at ROCLING-2025 Shared Task: Emotion-Space-Based System for Doctors’ Self-Reflection Sentiment Analysis](https://aclanthology.org/2025.rocling-main.45/) (Lin et al., ROCLING 2025)
ACL