A Transformer-based Hybrid Model for Implicit Emotion Recognition in Arabic Text

Authors

  • Hanane Boutouta Department of Computer Science, Faculty of Sciences, Ferhat Abbas University, Setif, Algeria | Artificial Intelligence Laboratory (AI-lab), Faculty of Sciences, Ferhat Abbas University, Setif, Algeria https://orcid.org/0009-0001-5255-0851
  • Abdelaziz Lakhfif Department of Computer Science, Faculty of Sciences, Ferhat Abbas University, Setif, Algeria | Artificial Intelligence Laboratory (AI-lab), Faculty of Sciences, Ferhat Abbas University, Setif, Algeria
  • Ferial Senator Department of Computer Science, Faculty of Sciences, Ferhat Abbas University, Setif, Algeria | Artificial Intelligence Laboratory (AI-lab), Faculty of Sciences, Ferhat Abbas University, Setif, Algeria
  • Chahrazed Mediani Department of Computer Science, Faculty of Sciences, Ferhat Abbas University, Setif, Algeria | Artificial Intelligence Laboratory (AI-lab), Faculty of Sciences, Ferhat Abbas University, Setif, Algeria
Volume: 15 | Issue: 3 | Pages: 23834-23839 | June 2025 | https://doi.org/10.48084/etasr.10261

Abstract

Implicit emotion recognition has emerged as an active area of research in modern Natural Language Processing (NLP). Unlike explicit emotions, which are directly expressed through emotional words, implicit emotions are inferred from the surrounding context, making their detection more challenging. While most research in Arabic NLP has focused on recognizing explicit emotions, the study of implicit emotions remains largely unexplored, primarily due to its unique linguistic and morphological characteristics. The current study addresses this gap by compiling an Arabic dataset for the implicit emotion recognition task, named Arabic Implicit Emotion Dataset (AIEmoD), which is curated from existing publicly available explicit emotion datasets. Furthermore, it proposes a novel hybrid deep learning model that integrates the Arabic transformer-based AraBERT model with a Bidirectional Gated Recurrent Units (BiGRU) network to recognize and classify implicit emotions in Arabic text. The proposed AraBERT-BiGRU model was evaluated on two widely used Arabic emotion datasets, AETD and SemEval-2018, in addition to the newly compiled AIEmoD dataset. The results show that the model achieved F1-scores of 79.87% on AETD and 70.67% on AIEmoD, significantly outperforming deep learning baseline methods. Moreover, the proposed model surpassed current state-of-the-art approaches for explicit emotion recognition, even when applied to the more challenging task of implicit emotion detection. These findings highlight the effectiveness and robustness of the proposed AraBERT-BiGRU model in recognizing implicit emotions in Arabic text.

Keywords:

natural language processing, implicit emotion recognition, Arabic transformer, AraBERT, BiGRU

Downloads

Download data is not yet available.

References

R. Klinger, O. De Clercq, S. Mohammad, and A. Balahur, "IEST: WASSA-2018 Implicit Emotions Shared Task," in Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Brussels, Belgium, 2018, pp. 31–42.

W. Q. A. Saif, M. K. Alshammari, B. A. Mohammed, and A. A. Sallam, "Enhancing Emotion Detection in Textual Data: A Comparative Analysis of Machine Learning Models and Feature Extraction Techniques," Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 16471–16477, Oct. 2024.

H. Elfaik and E. H. Nfaoui, "Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Arabic Text," Journal of Intelligent Systems, vol. 30, no. 1, pp. 395–412, Dec. 2020.

B. Zemni, M. Zitouni, F. Bouhadiba, and M. Almutairi, "On Ambiguity in the Arabic Language: Scrutinizing Translation Issues through Machine Translation from English and French into Arabic," Journal of Intercultural Communication, pp. 203–212, Mar. 2024.

O. Rabie and C. Sturm, "Feel the heat: Emotion detection in Arabic social media content," in The International Conference on Data Mining, Internet Computing, and Big Data (BigData2014), Kuala Lumpur, Malaysia, Nov. 2014, pp. 37–49.

W. A. Hussien, Y. M. Tashtoush, M. Al-Ayyoub, and M. N. Al-Kabi, "Are emoticons good enough to train emotion classifiers of Arabic tweets?," in 2016 7th International Conference on Computer Science and Information Technology (CSIT), Amman, Jordan, Jul. 2016, pp. 1–6.

A. Al-Khatib and S. R. El-Beltagy, "Emotional Tone Detection in Arabic Tweets," in Computational Linguistics and Intelligent Text Processing, 2018, vol. 10762, pp. 105–114.

S. Alzu’bi, O. Badarneh, B. Hawashin, M. Al-Ayyoub, N. Alhindawi, and Y. Jararweh, "Multi-Label Emotion Classification for Arabic Tweets," in 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), Granada, Spain, Oct. 2019, pp. 499–504.

H. Mulki, C. Bechikh Ali, H. Haddad, and I. Babaoglu, "Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification," in Proceedings of The 12th International Workshop on Semantic Evaluation, New Orleans, Louisiana, 2018, pp. 167–171.

N. Kalchbrenner, E. Grefenstette, and P. Blunsom, "A Convolutional Neural Network for Modelling Sentences." arXiv, Apr. 2014.

P. Liu, X. Qiu, and X. Huang, "Recurrent Neural Network for Text Classification with Multi-Task Learning." arXiv, May 2016.

A. Graves, "Long Short-Term Memory," in Supervised Sequence Labelling with Recurrent Neural Networks, vol. 385, Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 37–45.

T. Mikolov, M. Karafiát, L. Burget, J. Černocký, and S. Khudanpur, "Recurrent neural network based language model," in Interspeech 2010, Sep. 2010, pp. 1045–1048.

M. Abdullah and S. Shaikh, "TeamUNCC at SemEval-2018 Task 1: Emotion Detection in English and Arabic Tweets using Deep Learning," in Proceedings of The 12th International Workshop on Semantic Evaluation, New Orleans, Louisiana, 2018, pp. 350–357.

M. Abdullah, M. Hadzikadicy, and S. Shaikhz, "SEDAT: Sentiment and Emotion Detection in Arabic Text Using CNN-LSTM Deep Learning," in 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, Dec. 2018, pp. 835–840.

M. Rhanoui, M. Mikram, S. Yousfi, and S. Barzali, "A CNN-BiLSTM Model for Document-Level Sentiment Analysis," Machine Learning and Knowledge Extraction, vol. 1, no. 3, pp. 832–847, Jul. 2019.

H. Elfaik and E. H. Nfaoui, "Combining Context-Aware Embeddings and an Attentional Deep Learning Model for Arabic Affect Analysis on Twitter," IEEE Access, vol. 9, pp. 111214–111230, 2021.

A. E. Samy, S. R. El-Beltagy, and E. Hassanien, "A Context Integrated Model for Multi-label Emotion Detection," Procedia Computer Science, vol. 142, pp. 61–71, 2018.

A. Mansy, S. Rady, and T. Gharib, "An Ensemble Deep Learning Approach for Emotion Detection in Arabic Tweets," International Journal of Advanced Computer Science and Applications, vol. 13, no. 4, 2022.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding." arXiv, May 2019.

W. Antoun, F. Baly, and H. Hajj, "AraBERT: Transformer-based Model for Arabic Language Understanding." arXiv, Mar. 2021.

A. Rozental, D. Fleischer, and Z. Kelrich, "Amobee at IEST 2018: Transfer Learning from Language Models," in Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Brussels, Belgium, 2018, pp. 43–49.

J. Balazs, E. Marrese-Taylor, and Y. Matsuo, "IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations," in Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Brussels, Belgium, 2018, pp. 50–56.

A. Chronopoulou, A. Margatina, C. Baziotis, and A. Potamianos, "NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification," in Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Brussels, Belgium, 2018, pp. 57–64.

Q. Zhou and H. Wu, "NLP at IEST 2018: BiLSTM-Attention and LSTM-Attention via Soft Voting in Emotion Classification," in Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Brussels, Belgium, 2018, pp. 189–194.

H. Fei, Y. Ren, and D. Ji, "Implicit Objective Network for Emotion Detection," in Natural Language Processing and Chinese Computing, 2019, vol. 11838, pp. 647–659.

M. Noui, A. Lakhfif, and M. A. Laouadi, "Event Detection and Classification in Tweets using Deep Learning," Engineering, Technology & Applied Science Research, vol. 15, no. 1, pp. 19977–19982, Feb. 2025.

D. Li, J. Wang, and X. Zhang, "CIEA: A Corpus for Chinese Implicit Emotion Analysis," in 2019 International Conference on Asian Language Processing (IALP), Shanghai, Singapore, Nov. 2019, pp. 90–95.

Y. Qian, J. Wang, D. Li, and X. Zhang, "Interactive capsule network for implicit sentiment analysis," Applied Intelligence, vol. 53, no. 3, pp. 3109–3123, Feb. 2023.

S. Mohammad, F. Bravo-Marquez, M. Salameh, and S. Kiritchenko, "SemEval-2018 Task 1: Affect in Tweets," in Proceedings of The 12th International Workshop on Semantic Evaluation, New Orleans, Louisiana, 2018, pp. 1–17.

F. Bravo-Marquez, SemEval-2018 Task 1: Affect in Tweets (AIT-2018) – Datasets, CodaLab Competitions, 2018.

A. El Khatib, Emotional-Tone. GitHub repository.

A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov, "Bag of Tricks for Efficient Text Classification." arXiv, Aug. 2016.

M. J. Althobaiti, "Emotion Recognition in Arabic: A BERT-Based Transfer Learning Approach Leveraging Semantic Information of Online Comments," Journal of Theoretical and Applied Information Technology, vol. 101, no. 9, pp. 3270–3282, May 2023.

N. Alswaidan and M. E. B. Menai, "Hybrid Feature Model for Emotion Recognition in Arabic Text," IEEE Access, vol. 8, pp. 37843–37854, 2020.

Downloads

How to Cite

[1]
Boutouta, H., Lakhfif, A., Senator, F. and Mediani, C. 2025. A Transformer-based Hybrid Model for Implicit Emotion Recognition in Arabic Text. Engineering, Technology & Applied Science Research. 15, 3 (Jun. 2025), 23834–23839. DOI:https://doi.org/10.48084/etasr.10261.

Metrics

Abstract Views: 174
PDF Downloads: 107

Metrics Information