@inproceedings{linardatou-platanou-2025-angeliki,
title = "Angeliki Linardatou at {S}em{E}val-2025 Task 11: Multi-label Emotion Detection",
author = "Linardatou, Angeliki and
Platanou, Paraskevi",
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-1.70/",
pages = "502--507",
ISBN = "979-8-89176-273-2",
abstract = "This study, competing in SemEval 2025 Task 11 - Track A, detects anger, surprise, joy, fear, and sadness. We propose a hybrid approach combining fine-tuned BERT transformers, TF-IDF for lexical analysis, and a Voting Classifier (Logistic Regression, Random Forest, SVM, KNN, XG-Boost, LightGBM, CatBoost), with grid search optimizing thresholds. Our model achieves a macro F1-score of0.6864. Challenges include irony, ambiguity, and label imbalance. Future work will explore larger transformers, data augmentation, and cross-lingual adaptation. This research underscores the benefits of hybrid models, showing that combining deep learning with traditional NLP improves multi-label emotion detection."
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%0 Conference Proceedings
%T Angeliki Linardatou at SemEval-2025 Task 11: Multi-label Emotion Detection
%A Linardatou, Angeliki
%A Platanou, Paraskevi
%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 linardatou-platanou-2025-angeliki
%X This study, competing in SemEval 2025 Task 11 - Track A, detects anger, surprise, joy, fear, and sadness. We propose a hybrid approach combining fine-tuned BERT transformers, TF-IDF for lexical analysis, and a Voting Classifier (Logistic Regression, Random Forest, SVM, KNN, XG-Boost, LightGBM, CatBoost), with grid search optimizing thresholds. Our model achieves a macro F1-score of0.6864. Challenges include irony, ambiguity, and label imbalance. Future work will explore larger transformers, data augmentation, and cross-lingual adaptation. This research underscores the benefits of hybrid models, showing that combining deep learning with traditional NLP improves multi-label emotion detection.
%U https://aclanthology.org/2025.semeval-1.70/
%P 502-507
Markdown (Informal)
[Angeliki Linardatou at SemEval-2025 Task 11: Multi-label Emotion Detection](https://aclanthology.org/2025.semeval-1.70/) (Linardatou & Platanou, SemEval 2025)
ACL