A Proposed Deep Learning based Framework for Arabic Text Classification

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...

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Bibliographic Details
Published in:International journal of advanced computer science & applications Vol. 13; no. 8
Main Authors: Sayed, Mostafa, Abdelkader, Hatem, Khedr, Ayman E., Salem, Rashed
Format: Journal Article
Language:English
Published: West Yorkshire Science and Information (SAI) Organization Limited 01.01.2022
Subjects:
Adaptive learning
Algorithms
Arabic language
Artificial intelligence
Back propagation
Classification
Computer science
Computers
Datasets
Deep learning
Information retrieval
Machine learning
Text categorization
ISSN:2158-107X, 2156-5570
Online Access:Get full text
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Summary: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%.
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ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2022.0130836