@inproceedings{murrant-etal-2025-stfxnlp,
    title = "{STFXNLP} at {S}em{E}val-2025 Task 11 Track A: Neural Network, Schema, and Next Word Prediction-based Approaches to Perceived Emotion Detection",
    author = "Murrant, Noah  and
      Brooks, Samantha  and
      King, Milton",
    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-1.91/",
    pages = "651--656",
    ISBN = "979-8-89176-273-2",
    abstract = "In this work, we discuss our models that were applied to the SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection {\{}cite{\{}muhammad-etal-2025-semeval{\}}{\}}. We focused on the English data set of track A, which involves determining what emotions the reader of a snippet of text is feeling. We applied three different types of models that vary in their approaches and reported our findings on the task{'}s test set. We found that the performance of our models differed from each other, but neither of our models outperformed the task{'}s baseline model."
}