An Enhanced IHHO-LSTM Model for Predicting Online Public Opinion Trends in Public Health Emergencies

被引:4
作者
Mu, Guangyu [1 ,2 ,3 ]
Li, Jiaxue [1 ,3 ]
Liao, Zehan [1 ]
Yang, Ziye [1 ]
机构
[1] Jilin Univ Finance & Econ, Changchun, Peoples R China
[2] Key Lab Financial Technol Jilin Prov, Changchun, Peoples R China
[3] Jilin Univ Finance & Econ, Sch Management Sci & Informat Engn, Changchun 130117, Peoples R China
来源
SAGE OPEN | 2024年 / 14卷 / 02期
关键词
HHO algorithm; deep learning; IHHO-LSTM model; online public opinion; trend prediction; public health emergencies; SUPPORT VECTOR REGRESSION; INFORMATION; NETWORK;
D O I
10.1177/21582440241257681
中图分类号
C [社会科学总论];
学科分类号
03 ; 0303 ;
摘要
Social networks accelerate information communication in public health emergencies. Some negative information may cause an outbreak of public opinion crisis. Accurately predicting online public opinion trends can help the relevant departments take timely and effective measures to cope with risks. Therefore, this research proposes a prediction model incorporating the swarm intelligence optimization algorithm and the deep learning method. In this model, we improve the Harris Hawks Optimization (HHO) algorithm by introducing the Cauchy distribution function, the stochastic contraction exponential function, and the adaptive inertia weight. Then we utilize the improved HHO (IHHO) algorithm to optimize the hyperparameters of the deep learning method LSTM, including the learning rate and the number of neurons in the hidden layer. Finally, we construct the IHHO-LSTM model to make predictions in three public health emergencies. The experiments verify that the proposed model outperforms other single and hybrid models. The MAPE values reduce by 78.34%, 54.46%, and 46.42% relative to the average values of the three single models. Compared with the mean values of the two hybrid models, the MAPE values decrease by 47.69%, 18.45%, and 5.78%. The IHHO-LSTM model can be applied to public opinion early warning and reversal identification, providing a reference in public opinion management. [Purpose] Social networks accelerate information communication in public health emergencies. Some negative information may cause an outbreak of public opinion crisis. Accurately predicting online public opinion trends can help the relevant departments take timely and effective measures to cope with risks. [Method] This research proposes a prediction model incorporating the swarm intelligence optimization algorithm and the deep learning method. In this model, we improve the Harris Hawks Optimization (HHO) algorithm by introducing the Cauchy distribution function, the stochastic contraction exponential function, and the adaptive inertia weight. Then we utilize the improved HHO algorithm to optimize the hyperparameters of the deep learning method LSTM, including the learning rate and the number of neurons in the hidden layer. Finally, we construct the IHHO-LSTM model to make predictions in three public health emergencies. [Conclusion] The experiments verify that the proposed model outperforms other single and hybrid models. The MAPE values reduce by 78.34%, 54.46%, and 46.42% relative to the average values of the three single models. Compared with the mean values of the two hybrid models, the MAPE values decrease by 47.69%, 18.45%, and 5.78%. [Implication] The IHHO-LSTM model can be applied to public opinion early warning and reversal identification, providing a reference in public opinion management. [Limitation] For countries with underdeveloped social networks, the prediction of the proposed model may be biased.
引用
收藏
页数:16
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