@inproceedings{linardatou-platanou-2025-angeliki,
    title = "Angeliki Linardatou at {S}em{E}val-2025 Task 11: Multi-label Emotion Detection",
    author = "Linardatou, Angeliki  and
      Platanou, Paraskevi",
    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/corrections-2025-08/2025.semeval-1.70/",
    pages = "502--507",
    ISBN = "979-8-89176-273-2",
    abstract = "This study, competing in SemEval 2025 Task 11 - Track A, detects anger, surprise, joy, fear, and sadness. We propose a hybrid approach combining fine-tuned BERT transformers, TF-IDF for lexical analysis, and a Voting Classifier (Logistic Regression, Random Forest, SVM, KNN, XG-Boost, LightGBM, CatBoost), with grid search optimizing thresholds. Our model achieves a macro F1-score of0.6864. Challenges include irony, ambiguity, and label imbalance. Future work will explore larger transformers, data augmentation, and cross-lingual adaptation. This research underscores the benefits of hybrid models, showing that combining deep learning with traditional NLP improves multi-label emotion detection."
}