@inproceedings{wang-etal-2025-qmul,
title = "{QMUL} at {S}em{E}val-2025 Task 11: Explicit Emotion Detection with {E}mo{L}ex, Feature Engineering, and Threshold-Optimized Multi-Label Classification",
author = "Wang, Angeline and
Gupta, Aditya and
Roman, Iran and
Zubiaga, Arkaitz",
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://aclanthology.org/2025.semeval-1.128/",
pages = "959--964",
ISBN = "979-8-89176-273-2",
abstract = "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{\%}."
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<abstract>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%.</abstract>
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%0 Conference Proceedings
%T QMUL at SemEval-2025 Task 11: Explicit Emotion Detection with EmoLex, Feature Engineering, and Threshold-Optimized Multi-Label Classification
%A Wang, Angeline
%A Gupta, Aditya
%A Roman, Iran
%A Zubiaga, Arkaitz
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F wang-etal-2025-qmul
%X 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%.
%U https://aclanthology.org/2025.semeval-1.128/
%P 959-964
Markdown (Informal)
[QMUL at SemEval-2025 Task 11: Explicit Emotion Detection with EmoLex, Feature Engineering, and Threshold-Optimized Multi-Label Classification](https://aclanthology.org/2025.semeval-1.128/) (Wang et al., SemEval 2025)
ACL