Abstract: Deep learning has become one of the crucial trends in the modern era due to the huge amount of data that has become available. This paper aims to investigate and improve a generic framework for Arabic Text Classification (ATC) with different deep learning techniques. Besides, it deals directly with a word in its original style as a basic unit of modern Arabic sentence and on a different level of N-grams versus a combination of Intersected Consecutive Word proposed method (ICW). However, it aimed to discuss the results of the different experiments for the enhancements of the proposed method on different deep learning algorithms such as Scaled Conjugate Gradient (SCG) and Gradient descent with momentum and adaptive learning rate backpropagation (GDX) on ATC. The results showed that the proposed framework applied with the SCG algorithm and TF-IDF outperforms the GDX algorithm with an accuracy ratio of 90.65%.

Mostafa Sayed, Hatem Abdelkader, Ayman E. Khedr and Rashed Salem, “A Proposed Deep Learning based Framework for Arabic Text Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 13(8), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130836

@article{Sayed2022,
title = {A Proposed Deep Learning based Framework for Arabic Text Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130836},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130836},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {8},
author = {Mostafa Sayed and Hatem Abdelkader and Ayman E. Khedr and Rashed Salem}
}