@inproceedings{takahashi-etal-2025-ozemi,
    title = "{OZ}emi at {S}em{E}val-2025 Task 11: Multilingual Emotion Detection and Intensity",
    author = "Takahashi, Hidetsune  and
      Teng, Sumiko  and
      Lee, Jina  and
      Hu, Wenxiao  and
      Obe, Rio  and
      Yong, Chuen Shin  and
      Ohman, Emily",
    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/transition-to-people-yaml/2025.semeval-1.16/",
    pages = "109--115",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents the OZemi team{'}s submission to SemEval-2025 Task 11: Multilingual Emotion Detection and Intensity. Our approach prioritized computational efficiency, leveraging lightweight models that achieved competitive results even for low-resource languages. We addressed data imbalance through data augmentation techniques such as back translation and class balancing. Our system utilized multilingual BERT and machine translation to enhance performance across 35 languages. Despite ranking mid-tier overall, our results demonstrate that relatively simple models can yield adequate performance across diverse linguistic settings. We provide an error analysis of emotion classification challenges, particularly for nuanced expressions such as sarcasm and irony, and discuss the impact of emoji representation on model predictions. Finally, we outline future directions, including improvements in sentiment intensity modeling and the integration of semantic prosody to refine emotion detection."
}