Improving Sentiment Analysis with an Ensemble Transformers Model on Health Pandemic Based on Twitter Data

The widespread implementation of social distancing measures and remote work due to the COVID-19 pandemic has significantly altered societal dynamics, leading to an increased reliance on social media platforms for expressing sentiment. However, existing sentiment analysis models face challenges in comprehending the complexities of English tweets and nuances within social media conversations. To address this, our study proposes an innovative ensemble framework for sentiment analysis on social media, integrating Tiny Bert, a lightweight variant of BERT, into a dynamic bootstrap aggregation and stacking ensemble with extreme gradient boosting as a meta-learner. This framework aims to improve sentiment analysis efficiency while managing computational costs effectively. Our experiments demonstrate promising results, achieving an accuracy, precision, recall, and F1-score of 96.34%, 96.39%, 96.34%, and 96.35% respectively. These findings advance sentiment analysis tailored for the dynamic landscape of social media, enabling the identification of key pandemic discourse sentiments and informing public health interventions. The study underscores the importance of AI in extracting insights from COVID-19 tweets, contributing to a deeper understanding of societal impacts and highlighting its role in addressing global health challenges.