Please wait a minute...
Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (11): 59-67    DOI: 10.11925/infotech.2096-3467.2021.0525
Current Issue | Archive | Adv Search |
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
Download: PDF (1015 KB)   HTML ( 3
Export: BibTeX | EndNote (RIS)      
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.

Key wordsEmergencies      Online Public Opinion      CEEMDAN      Phase-space Reconstruction      Back Propagation Network     
Received: 25 May 2021      Published: 23 August 2021
ZTFLH:  C916  
Fund:National Social Science Fund of China(17CXW012)
Corresponding Authors: Feng Lanping,ORCID:0000-0003-2334-6621     E-mail: 19941415@hhu.edu.cn

Cite this article:

Cheng Tiejun, Wang Man, Huang Baofeng, Feng Lanping. Predicting Online Public Opinion in Emergencies Based on CEEMDAN-BP. Data Analysis and Knowledge Discovery, 2021, 5(11): 59-67.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0525     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I11/59

Process of the Model
Trend of Baidu Index for Three Emergencies
Decomposition Results of Baidu Index for Three Emergencies Based on CEEMDAN
参数

事件
新冠肺炎疫情 江苏响水爆炸 长春长生疫苗
τ 3 4 4
m 4 3 3
The Best Delay Time and Embedding Dimension
Prediction Results for Three Emergencies
案例事件 指标 CEEMDAN-BP CEEMDAN-SVM EMD-BP EMD-SVM BP SVM
新冠肺炎疫情 MAPE 8.60% 32.09% 22.86% 37.31% 24.68% 31.57%
PRMSE 9.79% 32.71% 26.08% 37.77% 15.29% 32.17%
TIC 0.047 0.141 0.117 0.160 0.072 0.139
江苏响水爆炸 MAPE 17.98% 89.21% 35.83% 185.12% 22.82% 44.55%
PRMSE 25.87% 93.96% 44.58% 185.09% 25.59% 44.20%
TIC 0.129 0.322 0.190 0.480 0.116 0.181
长春长生疫苗 MAPE 11.97% 30.93% 24.93% 28.95% 28.05% 27.31%
PRMSE 15.30% 33.40% 26.42% 32.20% 32.73% 27.45%
TIC 0.072 0.156 0.150 0.140 0.144 0.121
Performance Models of the Models
[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.
[1] Fan Tao,Wang Hao,Wu Peng. Sentiment Analysis of Online Users' Negative Emotions Based on Graph Convolutional Network and Dependency Parsing[J]. 数据分析与知识发现, 2021, 5(9): 97-106.
[2] Wang Nan,Li Hairong,Tan Shuru. Predicting of Public Opinion Reversal with Improved SMOTE Algorithm and Ensemble Learning[J]. 数据分析与知识发现, 2021, 5(4): 37-48.
[3] Han Pu, Zhang Wei, Zhang Zhanpeng, Wang Yuxin, Fang Haoyu. Sentiment Analysis of Weibo Posts on Public Health Emergency with Feature Fusion and Multi-Channel[J]. 数据分析与知识发现, 2021, 5(11): 68-79.
[4] Nie Lei,Fu Juan,Yi Chengqi,Yang Daoling. Measuring Enterprise’s Offline Resumption with Mobile Device Positioning Data[J]. 数据分析与知识发现, 2020, 4(7): 38-49.
[5] Deng Jiangao,Zhang Xuan,Fu Zhu,Wei Qingming. Tracking Online Public Opinion Based on System Dynamics: Case Study of “Xiangshui Explosion Accident”[J]. 数据分析与知识发现, 2020, 4(2/3): 110-121.
[6] Liang Yanping,An Lu,Liu Jing. Topic Resonance of Micro-blogs on Similar Public Health Emergencies[J]. 数据分析与知识发现, 2020, 4(2/3): 122-133.
[7] Lin Wang,Ke Wang,Jiang Wu. Public Opinion Propagation and Evolution of Public Health Emergencies in Social Media Era: A Case Study of 2018 Vaccine Event[J]. 数据分析与知识发现, 2019, 3(4): 42-52.
[8] Lu Bei, Cheng Xiao, Chen Zhi-Qun. Research on the Hot Topics Discovery Algorithm Based on Improved Ant Colony Clustering[J]. 现代图书情报技术, 2010, 26(4): 66-71.
[9] Wang Wei,Xu Xin. Online Public Opinion Hotspot Detection and Analysis Based on Document Clustering[J]. 现代图书情报技术, 2009, 3(3): 74-79.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn