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The rapid development of the Internet has given individuals the freedom to fully express their own opinions and value demands, and also accelerated the occurrence and evolution of online public opinions. In essence, online public opinion reflects social conditions and public opinions. In the era of big data, various kinds of data generated by the Internet every day are rapidly growing at the scale of PB level, which poses new challenges to the collation and analysis of online public opinion information. In order to comprehensively improve the authenticity and comprehensiveness of online public opinion governance, realize scientific guidance of public opinion, and optimize the way of online public opinion governance, this paper develops an online public opinion analysis system in the big data environment based on Hadoop architecture, and adopts data mining technology to analyze public opinion in three stages before, during and after the event. It evaluates the risks, predicts the development trend, puts forward early warning in time, and provides theoretical guidance for the governance reform of online public opinions.
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