@inproceedings{hussiny-etal-2024-persianemo,
title = "{P}ersian{E}mo: Enhancing {F}arsi-{D}ari Emotion Analysis with a Hybrid Transformer and Recurrent Neural Network Model",
author = "Hussiny, Mohammad Ali and
Payenda, Mohammad Arif and
{\O}vrelid, Lilja",
editor = "Melero, Maite and
Sakti, Sakriani and
Soria, Claudia",
booktitle = "Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.sigul-1.31",
pages = "257--263",
abstract = "Emotion analysis is a critical research domain within the field of natural language processing (NLP). While substantial progress has been made in this area for the Persian language, there is still a need for more precise models and larger datasets specifically focusing on the Farsi and Dari dialects. In this research, we introduce {``}LearnArmanEmo{''} as a new dataset and a superior ensemble approach for Persian text emotion classification. Our proposed model, which combines XLM-RoBERTa-large and BiGRU, undergoes evaluation on LetHerLearn for the Dari dialect, ARMANEMO for the Farsi dialect, and LearnArmanEmo for both Dari and Farsi dialects. The empirical results substantiate the efficacy of our approach with the combined model demonstrating superior performance. Specifically, our model achieves an F1 score of 72.9{\%} on LetHerLearn, an F1 score of 77.1{\%} on ARMANEMO, and an F1 score of 78.8{\%} on the LearnArmanEmo dataset, establishing it as a better ensemble model for these datasets. These findings underscore the potential of this hybrid model as a useful tool for enhancing the performance of emotion analysis in Persian language processing.",
}
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<abstract>Emotion analysis is a critical research domain within the field of natural language processing (NLP). While substantial progress has been made in this area for the Persian language, there is still a need for more precise models and larger datasets specifically focusing on the Farsi and Dari dialects. In this research, we introduce “LearnArmanEmo” as a new dataset and a superior ensemble approach for Persian text emotion classification. Our proposed model, which combines XLM-RoBERTa-large and BiGRU, undergoes evaluation on LetHerLearn for the Dari dialect, ARMANEMO for the Farsi dialect, and LearnArmanEmo for both Dari and Farsi dialects. The empirical results substantiate the efficacy of our approach with the combined model demonstrating superior performance. Specifically, our model achieves an F1 score of 72.9% on LetHerLearn, an F1 score of 77.1% on ARMANEMO, and an F1 score of 78.8% on the LearnArmanEmo dataset, establishing it as a better ensemble model for these datasets. These findings underscore the potential of this hybrid model as a useful tool for enhancing the performance of emotion analysis in Persian language processing.</abstract>
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%0 Conference Proceedings
%T PersianEmo: Enhancing Farsi-Dari Emotion Analysis with a Hybrid Transformer and Recurrent Neural Network Model
%A Hussiny, Mohammad Ali
%A Payenda, Mohammad Arif
%A Øvrelid, Lilja
%Y Melero, Maite
%Y Sakti, Sakriani
%Y Soria, Claudia
%S Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F hussiny-etal-2024-persianemo
%X Emotion analysis is a critical research domain within the field of natural language processing (NLP). While substantial progress has been made in this area for the Persian language, there is still a need for more precise models and larger datasets specifically focusing on the Farsi and Dari dialects. In this research, we introduce “LearnArmanEmo” as a new dataset and a superior ensemble approach for Persian text emotion classification. Our proposed model, which combines XLM-RoBERTa-large and BiGRU, undergoes evaluation on LetHerLearn for the Dari dialect, ARMANEMO for the Farsi dialect, and LearnArmanEmo for both Dari and Farsi dialects. The empirical results substantiate the efficacy of our approach with the combined model demonstrating superior performance. Specifically, our model achieves an F1 score of 72.9% on LetHerLearn, an F1 score of 77.1% on ARMANEMO, and an F1 score of 78.8% on the LearnArmanEmo dataset, establishing it as a better ensemble model for these datasets. These findings underscore the potential of this hybrid model as a useful tool for enhancing the performance of emotion analysis in Persian language processing.
%U https://aclanthology.org/2024.sigul-1.31
%P 257-263
Markdown (Informal)
[PersianEmo: Enhancing Farsi-Dari Emotion Analysis with a Hybrid Transformer and Recurrent Neural Network Model](https://aclanthology.org/2024.sigul-1.31) (Hussiny et al., SIGUL-WS 2024)
ACL