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Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

Ibrahim M. Alwayle1, Badriyya B. Al-onazi2, Jaber S. Alzahrani3, Khaled M. Alalayah1, Khadija M. Alaidarous1, Ibrahim Abdulrab Ahmed4, Mahmoud Othman5, Abdelwahed Motwakel6,*

1 Department of Computer Science, College of Science and Arts, Sharurah, Najran University, Najran, 55461, Saudi Arabia
2 Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Najran, 24211, Saudi Arabia
4 Computer Department, Applied College, Najran University, Najran, 66462, Saudi Arabia
5 Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, 11835, Egypt
6 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Abdelwahed Motwakel. Email: email

Computer Systems Science and Engineering 2023, 46(3), 3423-3438.


Arabic is one of the most spoken languages across the globe. However, there are fewer studies concerning Sentiment Analysis (SA) in Arabic. In recent years, the detected sentiments and emotions expressed in tweets have received significant interest. The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language. Two common models are available: Machine Learning and lexicon-based approaches to address emotion classification problems. With this motivation, the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification (TLBOML-ERC) model for Sentiment Analysis on tweets made in the Arabic language. The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets. To attain this, the proposed TLBOML-ERC model initially carries out data pre-processing and a Continuous Bag Of Words (CBOW)-based word embedding process. In addition, Denoising Autoencoder (DAE) model is also exploited to categorise different emotions expressed in Arabic tweets. To improve the efficacy of the DAE model, the Teaching and Learning-based Optimization (TLBO) algorithm is utilized to optimize the parameters. The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset. The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.


Cite This Article

APA Style
Alwayle, I.M., Al-onazi, B.B., Alzahrani, J.S., Alalayah, K.M., Alaidarous, K.M. et al. (2023). Parameter tuned machine learning based emotion recognition on arabic twitter data. Computer Systems Science and Engineering, 46(3), 3423-3438.
Vancouver Style
Alwayle IM, Al-onazi BB, Alzahrani JS, Alalayah KM, Alaidarous KM, Ahmed IA, et al. Parameter tuned machine learning based emotion recognition on arabic twitter data. Comput Syst Sci Eng. 2023;46(3):3423-3438
IEEE Style
I.M. Alwayle et al., "Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data," Comput. Syst. Sci. Eng., vol. 46, no. 3, pp. 3423-3438. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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