Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis

TLDR

Machine learning models are widely applied across disciplines, and proper hyperparameter tuning can significantly boost accuracy, yet extracting sentiment from Arabic’s complex morphology remains challenging. This study compares five hyperparameter tuning techniques—Grid Search, Random Search, Bayesian Optimization, Particle Swarm Optimization, and Genetic Algorithm—across six classifiers for Arabic sentiment classification. The authors evaluated the tuning methods on Logistic Regression, Ridge Classifier, SVC, Decision Tree, Random Forest, and Naive Bayes classifiers using a constructed Arabic sentiment dataset, measuring performance before and after tuning. Bayesian Optimization yielded the highest accuracy (95.62 %) with SVC, and the analysis highlights each technique’s strengths and limitations.

Abstract

Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained. In this paper, a comprehensive comparative analysis of various hyperparameter tuning techniques is performed; these are Grid Search, Random Search, Bayesian Optimization, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). They are used to optimize the accuracy of six machine learning algorithms, namely, Logistic Regression (LR), Ridge Classifier (RC), Support Vector Machine Classifier (SVC), Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB) classifiers. To test the performance of each hyperparameter tuning technique, the machine learning models are used to solve an Arabic sentiment classification problem. Sentiment analysis is the process of detecting whether a text carries a positive, negative, or neutral sentiment. However, extracting such sentiment from a complex derivational morphology language such as Arabic has been always very challenging. The performance of all classifiers is tested using our constructed dataset both before and after the hyperparameter tuning process. A detailed analysis is described, along with the strengths and limitations of each hyperparameter tuning technique. The results show that the highest accuracy was given by SVC both before and after the hyperparameter tuning process, with a score of 95.6208 obtained when using Bayesian Optimization.