A Study on the Evolution of Online Public Opinion During Major Public Health Emergencies Based on Deep Learning

This study investigates the evolution of online public opinion during the COVID-19 pandemic by integrating topic mining with sentiment analysis. To overcome the limitations of traditional short-text models and improve the accuracy of sentiment detection, we propose a novel hybrid framework that combines a GloVe-enhanced Biterm Topic Model (BTM) for semantic-aware topic clustering with a RoBERTa-TextCNN architecture for deep, context-rich sentiment classification. The framework is specifically designed to capture both the global semantic relationships of words and the dynamic contextual nuances of social media discourse. Using a large-scale corpus of more than 550,000 Weibo posts, we conducted comprehensive experiments to evaluate the model’s effectiveness. The proposed approach achieved an accuracy of 92.45%, significantly outperforming baseline transformer-based baseline representative of advanced contextual embedding models across multiple evaluation metrics, including precision, recall, F1-score, and AUC. These results confirm the robustness and stability of the hybrid design and demonstrate its advantages in balancing precision and recall. Beyond methodological validation, the empirical analysis provides important insights into the dynamics of online public discourse. User engagement is found to be highest for the topics directly tied to daily life, with discussions about quarantine conditions alone accounting for 42.6% of total discourse. Moreover, public sentiment proves to be highly volatile and event-driven; for example, the announcement of Wuhan’s reopening produced an 11% surge in positive sentiment, reflecting a collective emotional uplift at a major turning point of the pandemic. Taken together, these findings demonstrate that online discourse evolves in close connection with both societal conditions and government interventions. The proposed topic–sentiment analysis framework not only advances methodological research in text mining and sentiment analysis, but also has the potential to serve as a practical tool for real-time monitoring online opinion. By capturing the fluctuations of public sentiment and identifying emerging themes, this study aims to provide insights that could inform policymaking by suggesting strategies to guide emotional contagion, strengthen crisis communication, and promote constructive public debate during health emergencies.