@inproceedings{abyan-2025-aghna,
    title = "{AGHNA} at {S}em{E}val-2025 Task 11: Predicting Emotion and Its Intensity within a Text with {E}mo{BERT}a",
    author = "Abyan, Moh.",
    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-1.140/",
    pages = "1057--1063",
    ISBN = "979-8-89176-273-2",
    abstract = "This paper presents our system that have been developed for SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection. The system is able to do two sub-tasks: Track A, related to detecting emotion(s) in a given text; Track B, related to calculate intensity of emotion(s) in a given text. The system will have EmoBERTa as the model baseline, despite some minor differences used in the system approach between these tracks. With the system designed above, Track A achieved a Macro-F1 Score of 0.7372, while Track B achieved Average Pearson r Score of 0.7618."
}