Using data mining to improve the efficiency of government governance in the context of carbon neutrality is an important way to achieve the modernization of the national governance system. This study starts with the logic of carbon neutral issues, analyzes the factors and indicators that affect the effectiveness of social governance, and constructs the evaluation index system of government social governance efficiency based on data mining application under the background of carbon neutral, including per capita GDP, per capita domestic power consumption of residents, per capita CO2 emissions, per capita green area, industrial waste gas treatment rate, industrial wastewater discharge compliance rate and other indicators, which includes 4 first-class indicators, 19 second-class indicators and 38 third class indicators. Then, the CV-CRITICAL (coefficient of variation critical) index weight determination algorithm is used to determine the index weight. The Pearson correlation coefficient method is used to evaluate the correlation between the two vectors, and then the rationality of the government social governance efficiency evaluation index system based on data mining applications is evaluated. The evaluation results show that the level of social governance effectiveness of the Chinese government is on the rise from 2016 to 2021. This study promotes the application of improving the efficiency of government social governance in the context of carbon neutrality, and provides tools for relevant assessment through data mining technology. This research can not only deepen the theoretical connotation of government governance effectiveness, but also help promote the application of big data in government governance practice.
At present, social media have become the main media of network public opinion (PO) dissemination. By analyzing the trend of emotional development in public emergencies, we can explore the evolution law of PO and identify potential risks, which provide decision support for the guidance and control of government management. First, based on the concept of critical points in the complex system, this study established a public sentiment (PS) evolution model under public emergencies and proposed an algorithm to identify the critical points in PS based on microblog data analysis. In addition, the BC-BIRCH algorithm was used to construct a topic clustering model for public emergencies, which improved the effect of topic discovery by merging multiple topic clusters. The evolution of public emergencies was analyzed by calculating the emotional heat value of different topic events. Finally, experimental results showed that the emotion of netizens' fluctuates greatly in the initial stage of PO under different themes. The method used in this paper achieved good results in topic clustering, critical point prediction, and PO evolution analysis of public emergencies. The main contribution of this paper is to analyze the evolution of the internal mechanism of PS and to identify and predict key nodes such as the outbreak and extinction of netizens' sentiment based on data-driven methods so as to provide the basis and support to the government and related media as the main body of prevention and control to respond in advance and guide in time.
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