Title: Design of social media information extraction system based on deep learning

Authors: Huimin Wang; Yaping Gao

Addresses: Department of Preschool Education, Hebei Women's Vocational College, Shijiazhuang 050091, China ' Department of Modern Services, Hebei Women's Vocational College, Shijiazhuang 050091, China

Abstract: Aiming at the problems of low accuracy and long time in traditional systems, a social media information extraction system based on deep learning is designed. Firstly, the overall framework of the system is designed, including text extraction module, keyword extraction module and emotion analysis module. Then, the social media information is preprocessed, the emotional resource establishment and information extraction rules are constructed according to the preprocessing results, and the convolution neural network is used to construct the social media information extraction model. Finally, according to the correlation between text entries and categories, the global MI values of entries and all categories are calculated. The calculation results are inputted into the constructed convolution neural network model, and the social media information extraction results are output. The simulation results show that the extraction accuracy of the designed system is high and the extraction time is within 15 s.

Keywords: social media; information extraction; emotional resources; convolutional neural network; text entry.

DOI: 10.1504/IJWBC.2023.131387

International Journal of Web Based Communities, 2023 Vol.19 No.2/3, pp.161 - 174

Received: 26 Oct 2021
Accepted: 21 Feb 2022
Published online: 09 Jun 2023 *

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