@inproceedings{gundam-etal-2025-zero,
    title = "Zero at {S}em{E}val-2025 Task 11: Multilingual Emotion Classification with {BERT} Variants: A Comparative Study",
    author = "Gundam, Revanth  and
      Marri, Abhinav  and
      Mamidi, Radhika",
    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-1.156/",
    pages = "1181--1186",
    ISBN = "979-8-89176-273-2",
    abstract = "Emotion detection in text plays a very crucial role in NLP applications such as sentiment analysis and feedback analysis. This study tackles two tasks: multi-label emotion detection, where the goal is to classify text based on six emotions (joy, sadness, fear, anger, surprise, and disgust) in a multilingual setting, and emotion intensity prediction, which assigns an ordinal intensity score to each of the above-given emotions. Using the BRIGHTER dataset, a multilingual corpus spanning 28 languages, the paper addresses issues like class imbalances by treating each emotion as an independent binary classification problem. The paper first explores strategies such as static embeddings such as GloVe with logistic regression classifiers on top of it. To capture contextual nuances more effectively, we fine-tune transformer based models, such as BERT and RoBERTa. Our approach demonstrates the benefits of fine-tuning for improved emotion prediction, while also highlighting the challenges of multilingual and multi-label classification."
}