Title: Analysis of online marketing user participation preference attribute based on social network text mining

Authors: Lu Zhang; Wanqing Chen; Hengzhi Nie

Addresses: Department of Economics and Trade, Henan Polytechnic Institute, Nanyang, 473001, Hennan, China; Department of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430073, China ' Zhengzhou Fengyang Foreign Language School, Zhengzhou, 450000, Hennan, China ' The Academy of Digital China (FuJian), Fuzhou, 350003, China

Abstract: In order to improve the accuracy of online marketing users' participation preference feature attribute recognition, an analysis method of online marketing users' participation preference feature attribute based on social network text mining is proposed. Firstly, the TF-IDF algorithm is used to calculate the weight value of keywords in the tag, and then the user portrait of the social network platform is constructed after sorting. Then, the collaborative filtering algorithm is used to determine the user's preference characteristics for products containing keywords, and the K-L feature compressor is used to extract the user's participation preference characteristics of online marketing. Finally, the online marketing user participation preference characteristic attributes are classified to realise the analysis of online marketing user participation preference characteristic attributes. The experimental results show that the accuracy of this method is always above 90% and the average time is 3.88s.

Keywords: social networks; text mining; online marketing; preferential features; TF-IDF algorithm.

DOI: 10.1504/IJBIDM.2025.145381

International Journal of Business Intelligence and Data Mining, 2025 Vol.26 No.3/4, pp.473 - 489

Received: 29 Nov 2023
Accepted: 23 Aug 2024
Published online: 31 Mar 2025 *

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