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

Authors: Shumin Zhi; Lin Yu

Addresses: Department of Health Management, Zhengzhou Shuqing Medical College, Zhengzhou, 450064, Henan, China ' Department of Health Management, Zhengzhou Shuqing Medical College, Zhengzhou, 450064, Henan, China

Abstract: 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.

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

DOI: 10.1504/IJWBC.2024.142481

International Journal of Web Based Communities, 2024 Vol.20 No.3/4, pp.245 - 262

Received: 19 May 2023
Accepted: 10 Oct 2023
Published online: 04 Nov 2024 *

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