@inproceedings{nguyen-etal-2025-sentiment,
    title = "Sentiment Reasoning for Healthcare",
    author = "Nguyen, Khai-Nguyen  and
      Le-Duc, Khai  and
      Tat, Bach Phan  and
      Duy, Le  and
      Vo-Dang, Long  and
      Hy, Truong-Son",
    editor = "Rehm, Georg  and
      Li, Yunyao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-industry.82/",
    pages = "1175--1202",
    ISBN = "979-8-89176-288-6",
    abstract = "Transparency in AI healthcare decision-makingis crucial. By incorporating rationales to explain reason for each predicted label, userscould understand Large Language Models(LLMs){'}s reasoning to make better decision.In this work, we introduce a new task - Sentiment Reasoning - for both speech and textmodalities, and our proposed multimodal multitask framework and the world{'}s largest multimodal sentiment analysis dataset. Sentiment Reasoning is an auxiliary task in sentiment analysis where the model predicts boththe sentiment label and generates the rationale behind it based on the input transcript.Our study conducted on both human transcriptsand Automatic Speech Recognition (ASR) transcripts shows that Sentiment Reasoning helpsimprove model transparency by providing rationale for model prediction with quality semantically comparable to humans while alsoimproving model{'}s classification performance(+2{\%} increase in both accuracy and macro-F1) via rationale-augmented fine-tuning. Also,no significant difference in the semantic quality of generated rationales between human andASR transcripts. All code, data (five languages - Vietnamese, English, Chinese, German, andFrench) and models are published online."
}