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Predicting Online Public Opinion in Emergencies Based on CEEMDAN-BP |
Cheng Tiejun1,Wang Man1,Huang Baofeng1,Feng Lanping2() |
1School of Economics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China 2Business School, Hohai University, Changzhou 213022, China |
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Abstract [Objective] This paper tries to predict the development trend of online public opinion in emergencies. [Methods] First, we identified multiple uncertain factors affecting the evolution of online public opinion. Then, we constructed a CEEMDAN-BP prediction model combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, phase-space reconstruction and Back Propagation Network. Finally, we conducted an empirical study to examine the new model with three emergencies. [Results] Our CEEMDAN-BP model could better predict the development trend of online public opinion in emergencies. The average absolute errors of prediction in three emergencies were 8.60%, 17.98% and 11.97%, respectively. Our model’s prediction accuracy and stability were better than the existing ones. [Limitations] The experimental data was based on the daily statistics, which could not fully reflect the changing public opinion. [Conclusions] The CEEMDAN-BP model can effectively predict the development trend of online public opinion in emergencies, which helps related departments to prepare for and manage the emergencies.
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Received: 25 May 2021
Published: 23 August 2021
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Fund:National Social Science Fund of China(17CXW012) |
Corresponding Authors:
Feng Lanping,ORCID:0000-0003-2334-6621
E-mail: 19941415@hhu.edu.cn
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[1] |
中国互联网络信息中心(CNNIC). 第47次中国互联网络发展状况统计报告[EB/OL]. [2021-02-03]. http://cnnic.cn/gywm/xwzx/rdxw/20172017_7084/202102/t20210203_71364.htm.
|
[1] |
(China Internet Network Information Center(CNNIC). The 47th China Statistical Report on Internet Development [EB/OL]. [2021-02-03]. http://cnnic.cn/gywm/xwzx/rdxw/20172017_7084/202102/t20210203_71364.htm
|
[2] |
刘果. 新时代网络新闻舆论的时代特征与引导策略[J]. 湖南大学学报(社会科学版), 2019, 33(2):151-156.
|
[2] |
(Liu Guo. The Characteristics and Guidance Strategies of the Network News and Public Opinion in the New Era[J]. Journal of Hunan University (Social Sciences), 2019, 33(2):151-156.)
|
[3] |
Tsur O, Rappoport A. What’s in a Hashtag? Content based Prediction of the Spread of Ideas in Microblogging Communities[C]// Proceedings of the 5th International Conference on Web Search & Web Data Mining. ACM, 2012.
|
[4] |
罗泰晔. 基于Logistic模型的微博舆情热点发展预测研究[J]. 统计与信息论坛, 2017, 32(10):91-95.
|
[4] |
(Luo Taiye. Research on a Prediction Model of the Development of Microblog Hot Topics Based on Logistic Model[J]. Statistics & Information Forum, 2017, 32(10):91-95.)
|
[5] |
徐敏捷. 基于指数平滑法的微博舆情预测模型研究[J]. 中国公共安全(学术版), 2016(1):80-84.
|
[5] |
(Xu Minjie. Research on Microblogging Public Opinion Forecast Model Based on Exponential Smoothing[J]. China Public Security(Academy Edition), 2016(1):80-84.)
|
[6] |
滕文杰. 时间序列分析法在突发公共卫生事件网络舆情分析中的应用研究[J]. 中国卫生统计, 2014, 31(6):1071-1073.
|
[6] |
(Teng Wenjie. Research on the Prediction of Online Public Opinion base on Time Series[J]. Chinese Journal of Health Statistics, 2014, 31(6):1071-1073.)
|
[7] |
史蕊, 陈福集, 张金华. 基于组合灰色模型的网络舆情预测研究[J]. 情报杂志, 2018, 37(7):105-110.
|
[7] |
(Shi Rui, Chen Fuji, Zhang Jinhua. Prediction of Online Public Opinion Based on Combination Grey Model[J]. Journal of Intelligence, 2018, 37(7):105-110.)
|
[8] |
黄亚驹, 陈福集, 游丹丹. 基于混合算法和BP神经网络的网络舆情预测研究[J]. 情报科学, 2018, 36(2):24-29.
|
[8] |
(Huang Yaju, Chen Fuji, You Dandan. Research on Network Public Opinion Prediction Based on Hybrid Algorithm and BP Neural Network[J]. Information Science, 2018, 36(2):24-29.)
|
[9] |
游丹丹, 陈福集. 基于改进粒子群和BP神经网络的网络舆情预测研究[J]. 情报杂志, 2016, 35(8):156-161.
|
[9] |
(You Dandan, Chen Fuji. 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.)
|
[10] |
黄敏, 胡学刚. 基于支持向量机的网络舆情混沌预测[J]. 计算机工程与应用, 2013, 49(24):130-134.
|
[10] |
(Huang Min, Hu Xuegang. Internet Public Opinion Chaotic Prediction Based on Support Vector Regression Machine[J]. Computer Engineering and Applications, 2013, 49(24):130-134.)
|
[11] |
陈福集, 肖鸿雁. 基于改进ABC-BP模型的网络舆情热度预测研究[J]. 图书馆学研究, 2018(9):84-89.
|
[11] |
(Chen Fuji, Xiao Hongyan. Research on the Heat Prediction of Network Public Opinion Based on Improved ABC-BP Model[J]. Research on Library Science, 2018(9):84-89.)
|
[12] |
魏德志, 陈福集, 郑小雪. 基于混沌理论和改进径向基函数神经网络的网络舆情预测方法[J]. 物理学报, 2015, 64(11):52-59.
|
[12] |
(Wei Dezhi, Chen Fuji, Zheng Xiaoxue. 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.)
|
[13] |
高颖. 基于改进混沌理论的网络舆情短期预测策略方法研究[J]. 重庆理工大学学报(自然科学), 2019, 33(6):171-176.
|
[13] |
(Gao Ying. Prediction Strategy Method of Short-term Network Public Opinion Based on Improved Chaos Theory[J]. Journal of Chongqing University of Technology (Natural Science), 2019, 33(6):171-176.)
|
[14] |
聂黎生. 基于KPCA-粒子群随机森林算法的舆情趋势预测研究[J]. 现代电子技术, 2019, 42(15):79-82.
|
[14] |
(Nie Lisheng. Research on Trend Prediction of Public Opinion Based on KPCA and Particle Swarm Random Forest Algorithm[J]. Modern Electronics Technique, 2019, 42(15):79-82.)
|
[15] |
林育曼, 文海宁, 饶浩. 基于ARIMA-BP神经网络模型的微信舆情热度预测[J]. 统计与决策, 2019, 35(14):71-74.
|
[15] |
(Lin Yuman, Wen Haining, Rao Hao. WeChat Public Opinion Heat Forecasting Based on ARIMA-BP[J]. Statistics and Decision, 2019, 35(14):71-74.)
|
[16] |
Huang N E, Shen Z, Long S R, et al. The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis[J]. Proceedings of the Royal Society of London (Series A): Mathematical, Physical and Engineering Sciences, 1998, 454:903-995.
|
[17] |
Wu Z, Huang N E. Ensemble Empirical Mode Decomposition: A Noise—Assisted Data Analysis Method[J]. Advances in Adaptive Data Analysis, 2009, 1(1):1-41.
doi: 10.1142/S1793536909000047
|
[18] |
Yeh J R, Shieh J S, Huang N E . Complementary Ensemble Empirical Mode Decomposition: A Novel Noise Enhanced Data Analysis Method[J]. Advances in Adaptive Data Analysis, 2010, 2(2):135-156.
doi: 10.1142/S1793536910000422
|
[19] |
Torres M E, Colominas M A, Schlotthauer G, et al. A Complete Ensemble Empirical Mode Decomposition with Adaptive Noise[C]// Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2011.
|
[20] |
李亚男, 程志友. 基于CEEMDAN算法及NARX神经网络的短期负荷预测[J]. 安徽大学学报(自然科学版), 2021, 45(2):38-46.
|
[20] |
(Li Ya’nan, Cheng Zhiyou. Short Term Load Algorithm Forecasting Based and NARX Neural on CEEMDAN Network[J]. Journal of Anhui University (Natural Science Edition), 2021, 45(2):38-46.)
|
[21] |
林达, 杨招军. 我国股票与基金市场收益和风险的对比分析——基于CEEMDAN[J]. 预测, 2016, 35(1):55-61.
|
[21] |
(Lin Da, Yang Zhaojun. A Comparative Analysis of the Risk and Return Between Stocks and Funds-Based on CEEMDAN[J]. Forcasting, 2016, 35(1):55-61.)
|
[22] |
Packard N H, Crutchfield J P, Farmer J D. Geometry from a Time Series[J]. Physical Review Letters, 1980, 45(9):712-716.
doi: 10.1103/PhysRevLett.45.712
|
[23] |
Takens F. Detecting Strange Attractors in Turbulence[J]. Lecture Notes in Mathematics, 1981, 898:366-381.
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