aspg-3-3232-1791419279 20261008002759 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 17 2 Zahraa Fadhel Department of Computer Sciences, College of Science for Women, University of Babylon, Babylon, Iraq Hussien Attia Department of Computer Sciences, College of Science for Women, University of Babylon, Babylon, Iraq Yossra Hussain Ali Department of Computer sciences, University of Technology, Baghdad, Iraq The current Internet era is characterized by the widespread circulation of ideas and viewpoints among users across many social media platforms, such as microblogging sites, personal blogs, and reviews. Detecting fake reviews has become a widespread problem on digital platforms, posing a major challenge for both consumers and businesses. Due to the ever-increasing number of online reviews, it is no longer possible to manually identify fraudulent reviews. Artificial intelligence (AI) is essential in addressing the problem of identifying fake reviews. Feature extraction is a crucial stage in detecting fake reviews, and successful feature engineering techniques can significantly improve the accuracy of opinion extraction. The paper compares five feature extraction methods for multiple opinion classification using Twitter on airline and Borderland game reviews. FastText with X-GBoost classifier outperformed all other techniques, achieving 94.10% accuracy on the airline dataset and 100% accuracy in Borderland game reviews. 2025 161 172 3232 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.170212 https://www.americaspg.com/journal/3/article/3232 https://www.americaspg.com/storage/41728911747.pdf