Sentiment classification via improved feature selection using Boolean operator-based particle swarm optimization

Sentiment analysis plays a vital role in understanding user opinions across digital platforms. However, accurate classification in high-dimensional text data remains a significant challenge, primarily due to irrelevant and redundant features. This paper introduces a novel Boolean Operator-based Particle Swarm Optimization (BOPSO) algorithm that enhances the feature selection process for sentiment classification. Unlike traditional PSO, the proposed BOPSO integrates Boolean logic operators (Adder, Subtractor, XOR) into the velocity and position update equations to natively handle binary feature inclusion decisions, improving both exploration and exploitation in the search space. The model is evaluated on nine benchmark sentiment datasets, using five filter-based objective functions: Chi-Square, Correlation, Gain Ratio, Information Gain, and Symmetrical Uncertainty. Classification performance is assessed using Naïve Bayes, SVM, and ANN classifiers. The results demonstrate that BOPSO achieves an average accuracy improvement of 1.8% to 4.5% over state-of-the-art optimization techniques, including DE, GWO, ABC, and CS. Specifically, BOPSO with ANN achieves up to 100% accuracy on the laptop dataset and consistently outperforms others in precision, recall, and F1-score. This study confirms that BOPSO not only reduces feature dimensionality effectively but also improves sentiment classification performance significantly across domains.