Abstract
<jats:p>The COVID-19 pandemic triggered an unprecedented volume of real-time discourse on social media platforms, with Twitter serving as a global forum for public reactions, fears, and evolving narratives. Traditional sentiment analysis approaches treat tweets as independent, static samples, failing to capture the temporal evolution and geographic heterogeneity of public opinion. This paper presents a comprehensive spatio-temporal framework that integrates fine-grained sentiment classification using COVID-Twitter-BERT with dynamic topic modeling via BERTopic to automatically discover and track evolving narratives. Using a corpus of 2.4 million geolocated tweets collected between January 2020 and June 2022, our analysis reveals distinct pandemic phases: early fear-driven narratives about mask shortages (Q1 2020), vaccine optimism followed by polarization (2021), and pandemic fatigue (2022). Regional comparisons show significant differences, with US discourse dominated by freedom-versus-mandate debates while European discussions emphasized collective solidarity. Our framework achieved 76% F1-score in sentiment classification and successfully identified 50 distinct narratives with high coherence scores. This work provides a powerful methodology for real-time epidemiological narrative surveillance and crisis communication monitoring.</jats:p>