Research on Key Technologies of Public Opinion Monitoring and Early Warning of COVID-19 Prevention and Control in the Post-epidemic Era

In the post-epidemic era, public opinion monitoring and early warning for COVID-19 prevention and control have become vital research areas. This study aims to explore key technologies to enhance the capability of monitoring and early warning for public opinion regarding epidemic prevention and control. First, the study focuses on the collection and processing of public opinion data. By constructing a large-scale data collection system that integrates social media, news platforms, forums, and other channels, this study enables the real-time acquisition of public opinion information related to the epidemic. At the same time, natural language processing and text mining techniques are employed to clean, classify, and analyze the sentiment of large-scale text data, facilitating the extraction of valuable insights. Second, to enhance public opinion monitoring, this study introduces an emotion classification model based on deep learning. The model is compared with traditional machine learning approaches to evaluate its effectiveness in distinguishing the emotional tone of public opinion texts and analyzing individuals' attitudes and emotional responses toward the epidemic. To optimize performance, an early stopping mechanism is implemented during training to prevent overfitting, halting the process when validation loss ceases to improve after a specified number of iterations. Additionally, hyperparameter optimization is conducted using a grid search, systematically exploring various parameter combinations to identify the optimal configuration. Data balance is carefully maintained to enhance the model’s predictive accuracy and robustness, ensuring reliable and highquality results.