An improved approach to Arabic news classification based on hyperparameter tuning of machine learning algorithms

Abstract

This modern generation uses machine learning algorithms to tackle many issues, one of which is text classification. To develop classifiers that can predict the category of texts, and to optimize the output of these classifiers, hyperparameter tuning is required. The automatic optimization of the parameters of a machine learning model is referred to as hyperparameter tuning. Using NLP techniques and machine learning algorithms, we offer many methods in this research to improve the suggested classifier's accuracy values and identify the best hyperparameter. Grid search and random search are two methods for tweaking the hyperparameter. In any case, a comparison with other research works was made in order to assess the effectiveness of the suggested model and compare it against other models. The proposed approach appears to provide a robust solution for accurate Arabic text representation, interpretation, and categorization. It achieves the best performance using the CNN Arabic dataset in terms of overall accuracy, recall, precision, and F1-score by 95.16 %, 94.64 %,94.04 %, and 94.31 %.