Enhancing Arabic Text Classification: A Comparative Study of Machine Learning and Deep Learning Approaches

This paper presents a comprehensive pipeline that integrates machine learning (ML) and deep learning (DL) methods for Arabic text classification, achieving high accuracy. Our approach includes a detailed preprocessing phase,that involves tokenization, removal of non-Arabic words, stop words, punctuation, normalization of the Hamza character, and an optional stemming process. We utilize Bag of Words (BOW) and Term Frequency-Inverse Document Frequency (TF-IDF) for feature selection. Various ML and DL algorithms have been explored using the AL-Khaleej database, which contains 45500 arabic news documents. Performance evaluation on the testing set focuses on accuracy, precision, recall, and F1-score metrics. Our findings reveal that Logistic Regression and Stochastic Gradient Descent (SGD) classifiers perform robustly with both feature extraction methods, achieving 97% accuracy. Linear Support Vector Classifier (SVC) shows a slight improvement with TF-IDF. Stemming’s impact varies, with SVC improving to 98% accuracy without stemming using BOW. The Convolutional Neural Network (CNN) models, particularly with word-level representation, achieve superior accuracy of 97%, indicating their effectiveness in capturing contextual nuances and semantic relationships. Our pipeline not only accurately classifies Arabic texts but also provides insights into the efficacy of preprocessing and feature selection techniques in Arabic language processing. The success of our pipeline underscores its potential as a pivotal contribution to the advancement of Arabic text classification.