A Proposed Deep Learning based Framework for Arabic Text Classification

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%.

Text Classification Using Deep Learning Models: A Comparative Review

“…Managing and standardizing text data has become complex due to the rapid growth of unstructured online information and data. To address this issue, various machine learning and deep-learning algorithms have been developed to effectively process textual data and extract valuable insights from vast collections of information [11]. In recent studies, numerous deep learning approaches have been employed to address text classification challenges [12].…”