Abstract: Sentiment Analysis (SA) effectively examines big data, such as customer reviews, market research, social media posts, online discussions, and customer feedback evaluation. Arabic Language is a complex and rich language. The main reason for the need to enhance Arabic resources is the existence of numerous dialects alongside the standard version (MSA). This study investigates the impact of stemming and lemmatization methods on Arabic sentiment analysis (ASA) using Machine Learning techniques, specifically the LightGBM classifier. It also employs metaheuristic feature selection algorithms like particle swarm optimization, dragonfly optimization, grey wolf optimization, harris hawks optimizer, and a genetic optimization algorithm to identify the most relevant features to improve LightGBM’s model performance. It also employs the Optuna hyperparameter optimization framework to determine the optimal set of hyperparameter values to enhance LightGBM model performance. It also underscores the importance of preprocessing strategies in ASA and highlights the effectiveness of metaheuristic approaches and Optuna hyperparameter optimization in improving LightGBM model performance in ASA. It also applies different stemming and lemmatization methods, Metaheuristic Feature Selection algorithms, and the Optuna hyperparameter optimization on eleven datasets with different Arabic dialects. The findings indicate that metaheuristics feature selection with the LightGBM classifier, using suitable stemming and lemmatization or combining them, enhances LightGBM's accuracy by between 0 and 8%. Still, Optuna hyperparameter optimization with the LightGBM classifier, using suitable stemming and lemmatization or combining them, depending on data characteristics, improves LightGBM's accuracy by between 2 and 11%. It achieves superior results than metaheuristics feature selection in more than 90% of cases. This study is of significant importance in the field of ASA, providing valuable insights and directions for future research.

Mostafa Medhat Nazier, Mamdouh M. Gomaa, Mohamed M. Abdallah and Awny Sayed, “Arabic Sentiment Analysis Using Optuna Hyperparameter Optimization and Metaheuristics Feature Selection to Improve Performance of LightGBM” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160257

@article{Nazier2025,
title = {Arabic Sentiment Analysis Using Optuna Hyperparameter Optimization and Metaheuristics Feature Selection to Improve Performance of LightGBM},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160257},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160257},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Mostafa Medhat Nazier and Mamdouh M. Gomaa and Mohamed M. Abdallah and Awny Sayed}
}