QMUL at SemEval-2025 Task 11: Explicit Emotion Detection with EmoLex, Feature Engineering, and Threshold-Optimized Multi-Label Classification

AbstractSemEval 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%.