Enhanced Sentiment Classification Using a Hybrid Approach: Knowledge-Based Genetic, Relieff, and Partial Reinforcement Optimizer

Sentiment classification on social media data plays a crucial role in applications such as opinion mining, product analysis, and market forecasting. To overcome limitations in semantic understanding, and model robustness, a novel sentiment classification framework is introduced by integrating Human Memory Optimization with a Stacking Ensemble Fusion Voting (HMSEV) mechanism. A hybrid term frequency-inverse document frequency - bidirectional encoder representations from transformers approach effectively captures both statistical and contextual textual features. Additionally, an enhanced feature selection technique, knowledge-based genetic algorithm-relieff and partial reinforcement optimization, adaptively filters semantically rich, class-relevant features. The HMSEV model fuses CNN and BiLSTM outputs and refines them through human memory optimization, inspired by cognitive recall behavior, to strengthen relevant patterns and suppress noise. Ensemble voting stabilizes predictions across five sentiment classes. Implemented in Python and evaluated on the Amazon dataset, the proposed model achieves 98% accuracy, significantly outperforming existing approaches in both precision and generalizability.