@inproceedings{le-etal-2025-stanford,
    title = "{S}tanford {ML}ab at {S}em{E}val-2025 Task 11: Track {B}{--}Emotion Intensity Detection",
    author = "Le, Joseph  and
      Cui, Hannah  and
      Zhang, James",
    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-1.244/",
    pages = "1866--1869",
    ISBN = "979-8-89176-273-2",
    abstract = "We outline our SemEval 2025 Track B: Emotion Intensity Prediction submission, for which the objective is to predict the intensity of six primary emotions{---}anger, disgust, fear, joy, sadness, and surprise{---}between 0 and 3, with 0 being none and 3 being very strong. We used a regression fine-tuned BERT-based model that makes use of pretrained embeddings in order to sense subtle emotional wordings in text.We include tokenization with a BERT tokenizer, training with AdamW optimization, and an ExponentialLR scheduler used for learning rate modification. Performance is monitored based on validation loss and accuracy through closeness of model outputs to gold labels.Our best-performing model is 68.97{\%} accurate in validation and has a validation loss of 0.373, demonstrating BERT{'}s capability in fine-grained emotion intensity prediction. Key findings include that fine-tuning transformer models with regression loss improves prediction accuracy and that early stopping and learning rate scheduling avoid overfitting.Future improvements can include larger datasets, ensemble models, or other architectures such as RoBERTa and T5. This paper shows the potential of pretrained transformers for emotion intensity estimation and lays the groundwork for future computational emotion analysis research."
}