@inproceedings{abiola-etal-2025-cic-ipn,
    title = "{CIC}-{IPN} at {S}em{E}val-2025 Task 11: Transformer-Based Approach to Multi-Class Emotion Detection",
    author = "Abiola, Tolulope  and
      Ojo, Olumide Ebenezer  and
      Sidorov, Grigori  and
      Kolesnikova, Olga  and
      Calvo, Hiram",
    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-1.212/",
    pages = "1609--1615",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents a multi-step approach for multi-label emotion classification as our system description paper for the SEMEVAL-2025 workshop Task A using machine learning and deep learning models. We test our methodology on English, Spanish, and low-resource Yoruba datasets, with each dataset labeled with five emotion categories: anger, fear, joy, sadness, and surprise. Our preprocessing involves text cleaning and feature extraction using bigrams and TF-IDF. We employ logistic regression for baseline classification and fine-tune Transformer models, such as BERT and XLM-RoBERTa, for improved performance. The Transformer-based models outperformed the logistic regression model, achieving micro-F1 scores of 0.7061, 0.7321, and 0.2825 for English, Spanish, and Yoruba, respectively. Notably, our Yoruba fine-tuned model outperformed the baseline model of the task organizers with micro-F1 score of 0.092, demonstrating the effectiveness of Transformer models in handling emotion classification tasks across diverse languages."
}