Design of Hotspot Mining System for Social Media Network News and Public Opinion Based on Fuzzy Clustering Analysis

Due to the limitations of conventional data processing methods, traditional information analysis techniques often struggle to handle massive and rapidly disseminated information accurately and efficiently. This leads to low efficiency and poor accuracy in mining hot topics from social media news and public opinion. To address this, a system for mining hot topics in social media news and public opinion based on fuzzy clustering analysis is proposed. Following a hierarchical design logic, the overall architecture of the social media network news and public opinion hot spot mining system is constructed. A hardware architecture for embedded systems is designed to support multi-source data capture and resource loading, enhancing system performance. A semantic concept tree, built using a generalized mapping method, is employed to define hot news and public opinion information on social media networks. Utilizing a text tag structure as the core framework for the news and public opinion topic tag model facilitates the targeted collection of hot topic information. Innovatively, the fuzzy clustering analysis method is applied to cluster public opinion hot topics, determine the optimal clustering centers for these topics, and thereby achieve effective mining of news and public opinion hot topics. Experimental results demonstrate that the proposed system completes the hot topic mining process in 18.47 seconds. Furthermore, it achieves precision and recall rates above 95% across multiple categories of public opinion texts. This indicates that the designed system operates with high overall application efficiency and short processing times. It can comprehensively and accurately mine news and public opinion hot topics, effectively avoiding the misclassification of non-hot topics as hot topics, and exhibits strong practical application performance.