A method for tracing big data of network public opinion based on data mining algorithms

被引：0

作者：

Zhi, Shumin ^{[1
]}

Yu, Lin ^{[1
]}

机构：

[1] Department of Health Management, Zhengzhou Shuqing Medical College, Henan, Zhengzhou

来源：

International Journal of Web Based Communities | 2024年 / 20卷 / 3-4期

关键词：

big data; data mining algorithms; kernel fuzzy clustering; online public opinion; probability packet labelling; traceability methods;

D O I：

10.1504/IJWBC.2024.142481

中图分类号：

学科分类号：

摘要：

In order to achieve accurate traceability of massive public opinion data, this study carried out a study on the traceability method of network public opinion big data based on data mining algorithm. First of all, the network public opinion data is cleaned up and its data characteristics are mined. Then, the extracted public opinion features are taken as the input of the recursive neural network, which is used to construct the attention model and output the prediction results of the network public opinion. Finally, determine the network public opinion information to be tracked. Support vector machine is used to improve the probability packet tagging tracking algorithm and output the tracking results of public opinion information. The experimental results show that the implementation efficiency of this method is higher than 99%, and the average error of data tracing is less than 0.1, which has great application value. © 2024 Inderscience Enterprises Ltd.

引用

页码：245 / 262

页数：17

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