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Web of Proceedings - Francis Academic Press

Analytical Techniques of Network Public Opinion under Big Data Environment

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DOI: 10.25236/AISCT.2019.080


Fengxia Cong, Yongchang Ren

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Fengxia Cong


Through the analysis of public opinion, grasp the dynamics of public opinion in a timely manner, explore the law of public opinion generation and change, provide decision-makers with targeted and predictive public opinion information, improve the efficiency of decision-making, enlighten decision-making thinking. In the big data environment, it is a hot and difficult problem to analyze the massive network data and establish the public opinion monitoring and guidance mechanism, and to provide decision support for managers. Based on the comprehensive reference to domestic and foreign research results, this paper systematically summarizes and summarizes the network public opinion analysis techniques in the big data environment, including butterfly effect, K-means clustering algorithm, MapReduce programming model, BDI-Agent model and stakeholder theory. Provides a new perspective and solution for public opinion management.


Big data; network public opinion; analytical techniques; mathematical model