Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3232 2018 2018 Department of Computer Sciences, College of Science for Women, University of Babylon, Babylon, Iraq Zahraa Zahraa Department of Computer Sciences, College of Science for Women, University of Babylon, Babylon, Iraq Hussien Attia Department of Computer sciences, University of Technology, Baghdad, Iraq Yossra Hussain Ali 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 2025 161 172 10.54216/FPA.170212 https://www.americaspg.com/articleinfo/3/show/3232