@inproceedings{anand-etal-2025-databees,
    title = "{D}ata{B}ees at {S}em{E}val-2025 Task 11: Challenges and Limitations in Multi-Label Emotion Detection",
    author = "Anand, Sowmya  and
      Sriram, Tanisha  and
      Sivanaiah, Rajalakshmi  and
      S, Angel Deborah  and
      Thankanadar, Mirnalinee",
    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-1.33/",
    pages = "222--227",
    ISBN = "979-8-89176-273-2",
    abstract = "Text-based emotion detection is crucial in NLP,with applications in sentiment analysis, socialmedia monitoring, and human-computer interaction. This paper presents our approach tothe Multi-label Emotion Detection challenge,classifying texts into joy, sadness, anger, fear,and surprise. We experimented with traditionalmachine learning and transformer-based models, but results were suboptimal: F1 scores of0.3723 (English), 0.5174 (German), and 0.6957(Spanish). We analyze the impact of preprocessing, model selection, and dataset characteristics, highlighting key challenges in multilabel emotion classification and potential improvements."
}