QMUL at SemEval-2025 Task 11: Explicit Emotion Detection with EmoLex, Feature Engineering, and Threshold-Optimized Multi-Label Classification
Angeline Wang, Aditya Gupta, Iran R. Roman, Arkaitz Zubiaga
SemEval. 2025.
SemEval 2025 Task 11 Track A explores the detection of multiple emotions in text samples. Our best model combined BERT (fine-tuned on an emotion dataset) predictions and engineered features with EmoLex words appended. Together, these were used as input to train a multi-layer perceptron. This achieved a final test set Macro F1 score of 0.56. Compared to only using BERT predictions, our system improves performance by 43.6%.
@inproceedings{wang2025qmul,
title={QMUL at SemEval-2025 Task 11: Explicit Emotion Detection with EmoLex, Feature Engineering, and Threshold-Optimized Multi-Label Classification},
author={Wang, Angeline and Gupta, Aditya and Roman, Iran and Zubiaga, Arkaitz},
booktitle={Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)},
pages={959--964},
year={2025}
}
title={QMUL at SemEval-2025 Task 11: Explicit Emotion Detection with EmoLex, Feature Engineering, and Threshold-Optimized Multi-Label Classification},
author={Wang, Angeline and Gupta, Aditya and Roman, Iran and Zubiaga, Arkaitz},
booktitle={Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)},
pages={959--964},
year={2025}
}