aspg-2-3068-1791419350 20261008002910 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cybersecurity and Information Management JCIM 2769-7851 2690-6775 2025 15 1 Zahraa Fadhel Department of Computer Sciences, College of Science for Women, University of Babylon, Babylon, Iraq Hussien Attia Computer sciences department, College of Science for Women, University of Babylon, Iraq Yossra Hussain Ali Department of Computer Sciences, University of Technology, Baghdad, Iraq Fake review detection, often known as spam review detection, is a crucial aspect of natural language processing. It involves extracting valuable information from text documents obtained from various sources. Various methodologies, such as simple rule-based approaches, lexicon-based methods, and advanced machine learning algorithms, have been extensively employed with diverse classifiers to provide accurate detection of fake reviews. Nevertheless, review classification based on lexicons continues to face challenges in achieving high accuracies, mostly because of the need for domain-specific comprehensive dictionaries. Furthermore, machine learning-driven review detection also addresses the limitations in accuracy caused by the uncertainty of features in social data. In order To address the problem of accuracy, one effective approach is to carefully choose the most optimal set of features and minimize the number of features used. The Objective of the research paper is to select a small subset of features out of the thousands of features for accurate classification of spam review detection. Therefore, a good feature selection method is needed in order to speed up the processing rate and predictive accuracy. This paper, Harris Hawks Optimization (HHO), is utilized for feature selection in sentiment analysis tasks. The performance of the selected feature subsets was evaluated using SVM, X-GBoost, ETC classifiers. Experimental results on tweet reviews for the airline dataset demonstrated superior sentiment classification capabilities, achieving an accuracy of 0.9435% with SVM and 0.9607%, 0.9635% for X-Boost, ETC, respectively. 2025 11 21 3068 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCIM.150102 https://www.americaspg.com/journal/2/article/3068 https://www.americaspg.com/storage/71729687663.pdf