@inproceedings{kumar-etal-2025-indidataminer,
    title = "indi{D}ata{M}iner at {S}em{E}val-2025 Task 11: From Text to Emotion: Transformer-Based Models for Emotions Detection in {I}ndian Languages",
    author = "Kumar, Saurabh  and
      Kumar, Sujit  and
      Singh, Sanasam Ranbir  and
      Nandi, Sukumar",
    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-1.262/",
    pages = "2020--2027",
    ISBN = "979-8-89176-273-2",
    abstract = "Emotion detection is essential for applications like mental health monitoring and social media analysis, yet remains underexplored for Indian languages. This paper presents our system for SemEval-2025 Task 11 (Track A), focusing on multilabel emotion detection in Hindi and Marathi, two widely spoken Indian languages. We fine-tune IndicBERT v2 on the BRIGHTER dataset, achieving F1 scores of 87.37 (Hindi) and 88.32 (Marathi), outperforming baseline models. Our results highlight the effectiveness of fine-tuning a language-specific pretrained model for emotion detection, contributing to advancements in multilingual NLP research."
}