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Research on government network public opinion monitoring algorithm under the background of sustainable smart government

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  • Shiwei Zhang

Abstract

It is very necessary for the government to strengthen the supervision of network information. Considering the problems of over fitting and gradient disappearance in the traditional bi directional long short-term memory (BiLSTM) network, the regularisation method is used to adjust the input weight of the model. At the same time, 333 functions is used to replace tanh activation function to build a government network public opinion monitoring model of double-layer long short-term memory network (RLSTM). The model performance test results show that in dataset type 1, the public opinion prediction accuracy is 0.993, and in dataset type 2, the public opinion prediction accuracy is 0.982, and the prediction performance is the best. At the same time, the improved RLSTM model also has excellent performance in the test of model convergence effect and error performance. The research content is of great significance to strengthen the security supervision of network information.

Suggested Citation

  • Shiwei Zhang, 2023. "Research on government network public opinion monitoring algorithm under the background of sustainable smart government," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 28(2/3/4), pages 231-246.
    • Handle: RePEc:ids:ijnvor:v:28:y:2023:i:2/3/4:p:231-246
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