Enhanced Aquila Optimizer Algorithm for Efficient Stance Classification in Online Social Networks

Stance classification in Online Social Networks (OSNs) is essential to comprehend users' standpoints on various issues relating to social, political, and commercial aspects. However, traditional methods applied to large datasets and complex text structures usually face several challenges. This study introduces the Enhanced Aquila Optimizer (EAO), a metaheuristic algorithm designed to improve convergence and precision in stance classification tasks. EAO incorporates three new strategies: Opposition-Based Learning (OBL) to improve the exploration, Chaotic Local Search (CLS) to escape from the local optima, and a Restart Strategy (RS) to rejuvenate the search process. Experimental assessments on benchmark OSN datasets prove the superiority of EAO in terms of accuracy, precision, and computational efficiency compared to state-of-the-art methods. These findings position EAO as a potential revolution for stance classification and other large-scale text analysis tasks by offering a robust solution that can be used in real-time for complex OSN scenarios.