@inproceedings{waheed-etal-2025-habib,
    title = "Habib University at {S}em{E}val-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection",
    author = "Waheed, Owais  and
      Sajid, Hammad  and
      Chandani, Kushal  and
      Kazmi, Muhammad Areeb  and
      Kumar, Sandesh  and
      Samad, Abdul",
    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-1.217/",
    pages = "1656--1661",
    ISBN = "979-8-89176-273-2",
    abstract = "Emotion detection in text has emerged as a pivotal challenge in Natural Language Processing (NLP), particularly in multilingual and cross-lingual contexts. This paper presents our participation in SemEval 2025 Task 11, focusing on three subtasks: Multi-label Emotion Detection, Emotion Intensity Prediction, and Cross-lingual Emotion Detection. Leveraging state-of-the-art transformer models such as BERT and XLM-RoBERTa, we implemented baseline models and ensemble techniques to enhance predictive accuracy. Additionally, innovative approaches like data augmentation and translation-based cross-lingual emotion detection were used to address linguistic and class imbalances. Our results demonstrated significant improvements in F1 scores and Pearson correlations, showcasing the effectiveness of ensemble learning and transformer-based architectures in emotion recognition. This work advances the field by providing robust methods for emotion detection, particularly in low-resource and multilingual settings."
}