@inproceedings{almanza-etal-2025-verbanexai,
    title = "{V}erba{N}ex{AI} at {S}em{E}val-2025 Task 11 Track A: A {R}o{BERT}a-Based Approach for the Classification of Emotions in Text",
    author = "Almanza, Danileth  and
      Mart{\'i}nez Santos, Juan  and
      Puertas, Edwin",
    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-1.158/",
    pages = "1192--1197",
    ISBN = "979-8-89176-273-2",
    abstract = "Emotion detection in text has become a highly relevant research area due to the growing interest in understanding emotional states from human interaction in the digital world. This study presents an approach for emotion detection in text using a RoBERTa-based model, optimized for multi-label classification of the emotions joy, sadness, fear, anger, and surprise in the context of the SemEval 2025 - Task 11: Bridging the Gap in Text-Based Emotion Detection competition. Advanced preprocessing strategies were incorporated, including the augmentation of the training dataset through automatic translation to improve the representativeness of less frequent emotions. Additionally, a loss function adjustment mechanism was implemented to mitigate class imbalance, enabling the model to enhance its detection capability for underrepresented categories. The experimental results reflect competitive performance, with a macro F1 of 0.6577 on the development set and 0.6266 on the test set. In the competition, the model ranked 47th, demonstrating solid performance against the challenge posed."
}