CIC-IPN at SemEval-2025 Task 11: Transformer-Based Approach to Multi-Class Emotion Detection

AbstractThis 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.