Fuzzy-Based Ensemble Learning for Sentiment Analysis in Social Media Data

This research explores the effectiveness of integrating fuzzy logic with ensemble learning techniques for sentiment analysis in social media data. Traditional sentiment analysis methods often struggle with the informal and ambiguous nature of social media text. To address these challenges, we propose a fuzzybased ensemble learning model that combines the strengths of Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) networks with fuzzy logic. Our experimental results demonstrate that the fuzzy-based ensemble approach significantly outperforms individual models, including traditional machine learning and deep learning methods. The model achieves an accuracy of 89.0%, precision of 88.6%, recall of 89.2%, and an F1-score of 88.9%, highlighting its superior ability to manage the nuances and complexities of social media sentiment. This research contributes to the advancement of sentiment analysis by offering a robust framework that effectively handles ambiguity and improves predictive performance