Integrating swarm intelligence with deep learning for enhanced social media sentiment analysis

Abstract

Understanding user views on social media in the advent of internet content demands sentiment analysis. This study introduces a novel approach called particle swarm-accelerated model (PSAM), that integrates deep learning with long short-term memory (LSTM) with two hyper-parameters and swarm intelligence through particle swarm optimization (PSO). In the sentiment classification of YouTube movie reviews for “Pushpa 2,” the recommended approach classifies opinions as “positive,” “negative,” or “neutral,” with an accuracy score of 95.3%. The process involved utilizing YouTube API to collect user-genearted comments, followed by advanced preprocessing steps such as punctuation removal, stopword filtering, slang normalization, and emoji handling. PSO performs feature selection to boost the efficiency of classification systems. The PSAM model reaches superior outcome results compared to support vector machines (SVM), Naive Bayes, CNN, and random forest classifiers when evaluated based on F1-score and accuracy metrics. The proposed hybrid model demonstrates its ability to boost sentiment analysis in different social media platforms according to research findings.