  Sentiment analysis is crucial for understanding public opinion and consumer
behavior. Existing models face challenges with linguistic diversity,
generalizability, and explainability. We propose TRABSA, a hybrid framework
integrating transformer-based architectures, attention mechanisms, and BiLSTM
networks to address this. Leveraging RoBERTa-trained on 124M tweets, we bridge
gaps in sentiment analysis benchmarks, ensuring state-of-the-art accuracy.
Augmenting datasets with tweets from 32 countries and US states, we compare six
word-embedding techniques and three lexicon-based labeling techniques,
selecting the best for optimal sentiment analysis. TRABSA outperforms
traditional ML and deep learning models with 94% accuracy and significant
precision, recall, and F1-score gains. Evaluation across diverse datasets
demonstrates consistent superiority and generalizability. SHAP and LIME
analyses enhance interpretability, improving confidence in predictions. Our
study facilitates pandemic resource management, aiding resource planning,
policy formation, and vaccination tactics.
