@inproceedings{moreno-etal-2025-utbnlp,
    title = "{UTBNLP} at {S}emeval-2025 Task 11: Predicting Emotion Intensity with {BERT} and {VAD}-Informed Attention.",
    author = "Moreno, Melissa  and
      Mart{\'i}nez Santos, Juan  and
      Puertas, Edwin",
    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-1.162/",
    pages = "1217--1222",
    ISBN = "979-8-89176-273-2",
    abstract = "Emotion intensity prediction plays a crucial role in affective computing, allowing for a more precise understanding of how emotions are conveyed in text. This study proposes a system that estimates emotion intensity levels by integrating contextual language representations with numerical emotion-based features derived from Valence, Arousal, and Dominance (VAD). The methodology combines BERT embeddings, predefined VAD values per emotion, and machine learning techniques to enhance emotion detection, without relying on external lexicons. The system was evaluated on the SemEval-2025 Task 11 Track B dataset, predicting five emotions (anger, fear, joy, sadness, and surprise) on an ordinal scale.The results highlight the effectiveness of integrating contextual representations with predefined VAD values, enabling a more nuanced representation of emotional intensity. However, challenges arose in distinguishing intermediate intensity levels, affecting classification accuracy for certain emotions. Despite these limitations, the study provides insights into the strengths and weaknesses of combining deep learning with numerical emotion modeling, contributing to the development of more robust emotion prediction systems. Future research will explore advanced architectures and additional linguistic features to enhance model generalization across diverse textual domains."
}