Title: A feature extraction method of network social media data based on fuzzy mathematical model

Authors: Zong-biao Zhang

Addresses: Department of Education, Bo'zhou University, Bo'zhou, 236800, China

Abstract: Aiming at the problems of low extraction accuracy and efficiency of traditional network social media data feature extraction methods, this paper proposes a network social media data feature extraction method based on fuzzy mathematical model. Firstly, by constructing the distributed data topology model and integrating the adaptive distributed data reorganisation algorithm, the network social media data is collected. Then, the continuous attributes of network social media data are discretised by using data mining algorithm, and the correlation characteristics of network social media data are analysed. Finally, the fuzzy mathematical model is used to identify the correlation characteristics of online social media data, and the feature error is corrected by the correction function to complete the feature extraction of online social media data. The experimental results show that the accuracy of feature extraction of online social media data extracted by the proposed method is as high as 98.5%.

Keywords: fuzzy mathematical model; fuzzy set; membership function; artificial ant colony algorithm; online social media; data feature extraction.

DOI: 10.1504/IJWBC.2024.136671

International Journal of Web Based Communities, 2024 Vol.20 No.1/2, pp.15 - 26

Received: 09 Feb 2022
Accepted: 09 Jun 2022
Published online: 15 Feb 2024 *

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