@inproceedings{xie-etal-2025-rd,
    title = "{RD}-{MCSA}: A Multi-Class Sentiment Analysis Approach Integrating In-Context Classification Rationales and Demonstrations",
    author = "Xie, Haihua  and
      Cheng, Yinzhu  and
      Wang, Yaqing  and
      He, Miao  and
      Sun, Mingming",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1100/",
    pages = "21711--21734",
    ISBN = "979-8-89176-332-6",
    abstract = "This paper addresses the important yet underexplored task of **multi-class sentiment analysis (MCSA)**, which remains challenging due to the subtle semantic differences between adjacent sentiment categories and the scarcity of high-quality annotated data. To tackle these challenges, we propose **RD-MCSA** (**R**ationales and **D**emonstrations-based **M**ulti-**C**lass **S**entiment **A**nalysis), an In-Context Learning (ICL) framework designed to enhance MCSA performance under limited supervision by integrating classification rationales with adaptively selected demonstrations. First, semantically grounded classification rationales are generated from a representative, class-balanced subset of annotated samples selected using a tailored balanced coreset algorithm. These rationales are then paired with demonstrations chosen through a similarity-based mechanism powered by a **multi-kernel Gaussian process (MK-GP)**, enabling large language models (LLMs) to more effectively capture fine-grained sentiment distinctions. Experiments on five benchmark datasets demonstrate that RD-MCSA consistently outperforms both supervised baselines and standard ICL methods across various evaluation metrics."
}