@inproceedings{khatun-etal-2025-junlp,
    title = "{JUNLP}{\_}{S}arika at {S}em{E}val-2025 Task 11: Bridging Contextual Gaps in Text-Based Emotion Detection using Transformer Models",
    author = "Khatun, Sarika  and
      Saha, Dipanjan  and
      Das, Dipankar",
    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-1.275/",
    pages = "2116--2120",
    ISBN = "979-8-89176-273-2",
    abstract = "Because language is subjective, it can be difficult to infer human emotions from textual data. This work investigates the categorization of emotions using BERT, classifying five emotions{---}angry, fearful, joyful, sad, and surprised{---}by utilizing its contextual embeddings. Preprocessing techniques like tokenization and stop-word removal are used on the dataset, which comes from social media and personal tales. With a weighted F1-score of 0.75, our model was trained using a multi-label classification strategy. BERT has the lowest F1-score when it comes to rage, but it does well when it comes to identifying fear and surprise. The findings demonstrate the difficulties presented by unbalanced datasets while also highlighting the promise of transformer-based models for text-based emotion identification. Future research will use data augmentation methods, domain-adapted BERT models, and other methods to improve classification performance."
}