@inproceedings{oladepo-etal-2025-predicting,
    title = "Predicting Emotion Intensity in Text Using Transformer-Based Models",
    author = "Oladepo, Temitope  and
      Abiola, Oluwatobi  and
      Abiola, Tolulope  and
      -, Abdullah  and
      Muhammad, Usman  and
      Abiola, Babatunde",
    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-1.220/",
    pages = "1677--1682",
    ISBN = "979-8-89176-273-2",
    abstract = "Emotion intensity prediction in text enhances conversational AI by enabling a deeper understanding of nuanced human emotions, a crucial yet underexplored aspect of natural language processing (NLP). This study employs Transformer-based models to classify emotion intensity levels (0{--}3) for five emotions: anger, fear, joy, sadness, and surprise. The dataset, sourced from the SemEval shared task, was preprocessed to address class imbalance, and model training was performed using fine-tuned *bert-base-uncased*. Evaluation metrics showed that *sadness* achieved the highest accuracy (0.8017) and F1-macro (0.5916), while *fear* had the lowest accuracy (0.5690) despite a competitive F1-macro (0.5207). The results demonstrate the potential of Transformer-based models in emotion intensity prediction while highlighting the need for further improvements in class balancing and contextual representation."
}