@inproceedings{sarif-etal-2025-trans,
    title = "Trans-Sent at {S}em{E}val-2025 Task 11: Text-based Multi-label Emotion Detection using Pre-Trained {BERT} Transformer Models",
    author = "Sarif, Zafar  and
      Akhtar, Md  and
      Das, Dr. Abhishek  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/corrections-2025-08/2025.semeval-1.121/",
    pages = "893--898",
    ISBN = "979-8-89176-273-2",
    abstract = "We have introduced Trans-Sent, a Transformer-based model designed for multi-label emotion classification in SemEval-2025 Task 11. The model predicts perceived emotions such as joy, sadness, anger, fear, surprise, and disgust from text across seven languages, including Amharic, German, English, Hindi, Marathi, Russian, and Romanian. To handle data imbalance, the system incorporates preprocessing techniques, SMOTE oversampling, and feature engineering to enhance classification accuracy. The model was trained using the BRIGHTER and EthioEmo datasets, which contain diverse textual sources, such as social media, news, literature, and personal narratives. Traditional machine learning models, including Logistic Regression and Decision Trees, were tested but proved inadequate for multi-label classification due to their limited ability to capture contextual and semantic meaning. Fine-tuned BERT models demonstrated superior performance, with Russian achieving the highest ranking (9th overall), while languages with complex grammar, such as German and Amharic, performed lower. Future enhancements may include advanced data augmentation, cross-lingual learning, and multimodal emotion analysis to improve classification across different languages. Trans-Sent contributes to NLP by advancing multi-label emotion detection, particularly in underrepresented languages."
}