@inproceedings{bogatyreva-etal-2025-qimp,
title = "{Q}i{MP} at {S}em{E}val-2025 Task 11: Optimizing Text-based Emotion Classification in {E}nglish Beyond Traditional Methods",
author = "Bogatyreva, Mariia and
Gaertner, Pascal and
Ribas, Quim and
Dementieva, Daryna and
Fraser, Alexander",
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-1.298/",
pages = "2283--2296",
ISBN = "979-8-89176-273-2",
abstract = "As human-machine interactions become increasingly natural through text, accurate emotion recognition is essential. Detecting emotions provides valuable insights across various applications. In this paper, we present our approach for SemEval-2025 Task 11, Track A, which focuses on multi-label text-based detection of perceived emotions. Our system was designed for and tested on English language text. To classify emotions present in text snippets, we initially experimented with traditional techniques such as Logistic Regression, Gradient Boosting, and SVM. We then explored state-of-the-art LLMs (OpenAI o1 and DeepSeek V3) before developing our final system, a fine-tuned Transformer-based model. Our best-performing approach employs an ensemble of fine-tuned DeBERTa-large instances with multiple seeds, optimized using Optuna and StratifiedKFold cross-validation. This approach achieves an F1-score of 0.75, demonstrating promising results with room for further improvement."
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<abstract>As human-machine interactions become increasingly natural through text, accurate emotion recognition is essential. Detecting emotions provides valuable insights across various applications. In this paper, we present our approach for SemEval-2025 Task 11, Track A, which focuses on multi-label text-based detection of perceived emotions. Our system was designed for and tested on English language text. To classify emotions present in text snippets, we initially experimented with traditional techniques such as Logistic Regression, Gradient Boosting, and SVM. We then explored state-of-the-art LLMs (OpenAI o1 and DeepSeek V3) before developing our final system, a fine-tuned Transformer-based model. Our best-performing approach employs an ensemble of fine-tuned DeBERTa-large instances with multiple seeds, optimized using Optuna and StratifiedKFold cross-validation. This approach achieves an F1-score of 0.75, demonstrating promising results with room for further improvement.</abstract>
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%0 Conference Proceedings
%T QiMP at SemEval-2025 Task 11: Optimizing Text-based Emotion Classification in English Beyond Traditional Methods
%A Bogatyreva, Mariia
%A Gaertner, Pascal
%A Ribas, Quim
%A Dementieva, Daryna
%A Fraser, Alexander
%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 bogatyreva-etal-2025-qimp
%X As human-machine interactions become increasingly natural through text, accurate emotion recognition is essential. Detecting emotions provides valuable insights across various applications. In this paper, we present our approach for SemEval-2025 Task 11, Track A, which focuses on multi-label text-based detection of perceived emotions. Our system was designed for and tested on English language text. To classify emotions present in text snippets, we initially experimented with traditional techniques such as Logistic Regression, Gradient Boosting, and SVM. We then explored state-of-the-art LLMs (OpenAI o1 and DeepSeek V3) before developing our final system, a fine-tuned Transformer-based model. Our best-performing approach employs an ensemble of fine-tuned DeBERTa-large instances with multiple seeds, optimized using Optuna and StratifiedKFold cross-validation. This approach achieves an F1-score of 0.75, demonstrating promising results with room for further improvement.
%U https://aclanthology.org/2025.semeval-1.298/
%P 2283-2296
Markdown (Informal)
[QiMP at SemEval-2025 Task 11: Optimizing Text-based Emotion Classification in English Beyond Traditional Methods](https://aclanthology.org/2025.semeval-1.298/) (Bogatyreva et al., SemEval 2025)
ACL