Network Public Opinion Monitoring Method Based on Big Data Mining Technology

In order to meet the challenges of real-time monitoring and accuracy in online public opinion analysis, this article applied an online public opinion monitoring method based on big data mining technology. A dedicated monitoring system is built and a gray prediction model was integrated into it for the purpose of conducting a trend analysis and correcting the monitoring results. In addition, network hot issues were collected for simulation testing. The experimental results showed that the monitoring accuracy of this system was significantly improved, by 8.87%, and that it can accurately track the evolution of public opinion. The monitoring system can also effectively identify emerging topics, analyze public emotional trends, and warn of potential crises, thereby promoting active intervention in the management of public opinion and helping maintain social security and stability. The integration of data mining technology into this system solves the problem of inefficiency involved in manual monitoring and provides a scalable solution for the real-time analysis of cross-platform and heterogeneous online data sources.