@inproceedings{shenpo-2025-deep,
    title = "Deep at {S}em{E}val-2025 Task 11: A Multi-Stage Approach to Emotion Detection",
    author = "Shenpo, Dong",
    editor = "Rosenthal, Sara  and
      Ros{\'a}, Aiala  and
      Ghosh, Debanjan  and
      Zampieri, Marcos",
    booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-08/2025.semeval-1.254/",
    pages = "1957--1963",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents a novel text-based emotion detection approach for low-resource languages in SemEval-2025 Task 11. We fine-tuned Google Gemma 2 using tailored data augmentation and Chain-of-Thought prompting. Our method, incorporating supervised fine-tuning and model ensembling, significantly improved multi-label emotion recognition, intensity prediction, and cross-lingual performance. Results show strong performance in diverse low-resource settings. Challenges remain in fine-grained sentiment analysis. Future work will explore advanced data augmentation and knowledge transfer methods. This research demonstrates the potential of large language models for inclusive emotion analysis."
}