Network public opinion forecasting method fusing microblog hotspot analysis and LSTM model
1. College of Computer Science and Technology, Sanjiang University, Nanjing, Jiangsu 210012, China； 2. School of Information Engineering, Nanjing Audit University, Nanjing, Jiangsu 211815, China; 3. School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
Abstract：Nowadays， the Internet not only becomes an important platform for the public to obtain information and express views, but also brings the risk of social public opinion events. By predicting the trend of network public opinion in advance, the development of hot events can be accurately judged to provide suggestions to relevant departments of government for dealing with public opinion crisis. To solve the problems of poor prediction of single prediction model and great influence of social media on the trend of public opinion, a public opinion prediction method was proposed based on microblog hotspot analysis and LSTM neural network. The network public opinion prediction system was constructed for public opinion time series data analysis by web crawler and PyTorch machine learning platform. Considering the strong currency of microblog, the microblog heat score was calculated with the network hotspot analysis technology.LSTM network was improved, and MH-LSTM prediction model with two hidden layers was designed. Applying MH-LSTM model into the quantitative prediction of Baidu index of public opinion events, the experiments show the correctness with good prediction effect of the proposed model.
XU M J, LAN Y X, LIU B Y. Research on the prediction model of network public opinion based on combination forecasting method[J]. Information Science, 2016,34(12):40-45，87. （in Chinese）
ZHAO L J. Logistic curve in the application of network public opinion prediction research-in "guangxi cadmium pollution" the case of network public opinion[J]. Network Security Technology & Application, 2014(6):11-12. （in Chinese）
XIANG J P, ZUO J, QIAO S J, et al. Trend analysis of network public opinions[J]. Journal of Sichuan University(Natural Science Edition), 2013,50(5):985-990. （in Chinese）
SU C, PENG J, LI S G. The internet public opinion propagation model via uncertain differential equation[J]. Systems Engineering-Theory & Practice, 2015,35(12):3201-3209. （in Chinese）
WEI D Z, CHEN F J, ZHENG X X. Internet public opinion chaotic prediction based on chaos theory and the improved radial basis function in neural networks[J]. Acta Physica Sinica, 2015,64(11):52-59. （in Chinese）
YOU D D, CHEN F J. Research on the prediction of network public opinion based on improved PSO and BP neural network[J]. Journal of Intelligence, 2016,35(8):156-161. （in Chinese）
HUANG R P, ZUO W M, BI L Y. Predicting the stock market based on microblog mood[J]. Journal of Industrial Engineering and Engineering Management, 2015,29(1):47-52，215. （in Chinese）
YAO H. Tourism supply side analysis based on grey prediction model： a case study on the micro index of tourism strategy in sina microblog[J]. Tourism Forum, 2016,9(4):79-83. （in Chinese）
［12］
HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
SUN C H, HU B, ZOU Y X. A BP-LSTM trend forecast model for stock index[J]. Journal of Sichuan University(Natural Science Edition), 2020,57(1):27-31. （in Chinese）
［14］
ABADI M, BARHAM P, CHEN J, et al. TensorFlow: a system for large-scale machine learning[C]∥ Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, 2016: 265-283.
［15］
PASZKE A, GROSS S, MASSA F, et al. PyTorch: an imperative style, high-performance deep learning library[C]∥Advances in Neural Information Processing Systems, 2019: 8024-8035.
CHENG K F, DENG X J, ZHOU K, et al. Hot topic detection model based on multi-dimensions and comprehensive weights of microblog[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2019,31(4):468-475. （in Chinese）
LU Y, LI H K, SUN G Z. Distributed microblog crawler system based on P2P[J]. Journal of Jiangsu University (Natural Science Edition), 2016,37(3):296-301. （in Chinese）
［18］
戴元初.二八定律与舆情危机管理的重心选择[J].青年记者,2011(7):63-64.
DAI Y C. Pareto′s principle and the choice of the focus of public opinion crisis management[J]. Youth Journalist, 2011(7):63-64. （in Chinese）
ZENG X L, MA J, ZHU Y P, et al. Construction of protein decoy sequence database based on Attention Bi-LSTM model[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 2020,32(4):655-663.（in Chinese）
2021, Vol. 42 
