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The predicting public sentiment evolution on public emergencies under deep learning and internet of things

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Abstract

With the Internet's rapid development and the increasing amount of netizens, social contradictions frequently manifest over the Internet. Public emergencies develop and spread constantly online. Thus, it is of great significance to reasonably address the Online Public Sentiment (OPS) in the current critical stage of social transformation. The aim is to create a safe and credible network environment and realize the modern transformation of the dynamic evolution of OPS in public emergencies. Firstly, this paper expounds on the blocking process of the OPS evolution on public emergencies according to the Internet of Things-native big data. Then, it discusses the algorithm process of the Long Short-Term Memory (LSTM) Neural Network (NN) model. Further, it optimizes the LSTM NN model using the Adaptive Momentum Estimation (Adam). Finally, it simulates and predicts the OPS evolution using Artificial Intelligence technology and big data. The results show that the Adam-optimized LSTM NN model can predict the hotness of OPS in the dynamic evolution with high prediction accuracy. In predicting OPS evolution, the Mean Relative Errors (MRE) of the proposed Adam-LSTM, LSTM, and Backpropagation NN models are 0.06, 0.10, and 0.14, respectively. The proposed Adam-LSTM model presents the least MRE on the hotness of OPS. The relevant governments can refer to model-predicted OPS evolution to control public emergencies and OPS through the IoT. Therefore, the proposed Adam-LSTM model is feasible for predicting the OPS hotness. The finding has particular research significance for employing the LSTM model under the IoT in predicting the OPS evolution in public emergencies. Lastly, the OPS on public emergencies can be effectively guided thanks to the proposed Adam-LSTM prediction model and time nodes.

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Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

References

  1. Cui H, Kertész J (2022) Born in Rome or Sleeping Beauty: Emergence of hashtag popularity on a microblogging site. arXiv preprint arXiv, 2203(1):14802. https://doi.org/10.48550/arXiv.2203.14802

  2. Miravet-Verde S, Burgos R, Delgado J et al (2020) FASTQINS and ANUBIS: two bioinformatic tools to explore facts and artifacts in transposon sequencing and essentiality studies. Nucleic Acids Res 48(17):e102–e102. https://doi.org/10.1093/nar/gkaa679

    Article  Google Scholar 

  3. Song S, Guo Z, Wang X et al (2021) RETRACTED ARTICLE: The correlation between social transformation economic risk and Internet public opinion. Behav Inf Technol 40(7):723–733. https://doi.org/10.1080/0144929X.2020.1722750

    Article  Google Scholar 

  4. Larrouquere L, Gabin M, Poingt E et al (2020) Genesis of an emergency public drug information website by the French Society of Pharmacology and Therapeutics during the COVID-19 pandemic. Fundam Clin Pharmacol 34(3):389–396. https://doi.org/10.1111/fcp.12564

    Article  Google Scholar 

  5. Muniz-Rodriguez K, Ofori SK, Bayliss LC et al (2020) Social media use in emergency response to natural disasters: a systematic review with a public health perspective. Disaster Med Public Health Prep 14(1):139–149. https://doi.org/10.1017/dmp.2020.3

    Article  Google Scholar 

  6. Merkley E, Bridgman A, Loewen PJ et al (2020) A rare moment of cross-partisan consensus: Elite and public response to the COVID-19 pandemic in Canada. Can J Polit Sci/Revue Canadienne de science politique 53(2):311–318. https://doi.org/10.1017/S0008423920000311

    Article  Google Scholar 

  7. Boon-Itt S, Skunkan Y (2020) Public perception of the COVID-19 pandemic on Twitter: sentiment analysis and topic modeling study. JMIR Public Health Surveillance, 6(4):e21978. https://doi.org/10.2196/21978

  8. Zhang W, Wang Y, Yang L et al. (2020) Suspending classes without stopping learning: China's education emergency management policy in the COVID-19 outbreak. J Risk Financ Manage 13(3):55. https://doi.org/10.3390/jrfm13030055

  9. Wang B, Wang E, Zhu Z et al. (2021) An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors. Int J Distrib Sens Netw 17(10):15501477211033765. https://doi.org/10.1177/15501477211033765

  10. Shen L, Yao R, Zhang W et al (2021) Emotional attitudes of Chinese citizens on social distancing during the COVID-19 outbreak: analysis of social media data. JMIR Med Inf 9(3):e27079. https://doi.org/10.2196/27079

  11. Xiao Y, Li J, Zhu Y et al (2020) User Behavior prediction of social hotspots based on multimessage interaction and neural network. IEEE Trans Comput Soc Syst 7(2):536–545. https://doi.org/10.1109/TCSS.2020.2969484

    Article  Google Scholar 

  12. Tavoschi L, Quattrone F, D’Andrea E et al (2020) Twitter as a sentinel tool to monitor public opinion on vaccination: an opinion mining analysis from September 2016 to August 2017 in Italy. Hum Vaccin Immunother 16(5):1062–1069. https://doi.org/10.1080/21645515.2020.1714311

    Article  Google Scholar 

  13. Yadav A, Vishwakarma DK (2020) Sentiment analysis using deep learning architectures: a review. Artif Intell Rev 53(6):4335–4385. https://doi.org/10.1007/s13042-018-0799-4

    Article  Google Scholar 

  14. Jain PK, Yekun EA, Pamula R et al (2021) Consumer recommendation prediction in online reviews using Cuckoo optimized machine learning models. Comput Electr Eng 95(1):107397. https://doi.org/10.1016/j.compeleceng.2021.107397

    Article  Google Scholar 

  15. Munawar H S, Qayyum S, Ullah F et al. (2020) Big data and its applications in smart real estate and the disaster management life cycle: a systematic analysis. Big Data Cognit Comput 4(2:4. https://doi.org/10.3390/bdcc4020004

  16. Murphy J, Link MW, Childs JH et al (2014) Social media in public opinion research: Executive summary of the AAPOR task force on emerging technologies in public opinion research. Public Opin Q 78(4):788–794. https://doi.org/10.1093/poq/nfu053

    Article  Google Scholar 

  17. Han X, Wang J, Zhang M et al (2020) Using social media to mine and analyze public opinion related to COVID-19 in China. Int J Environ Res Public Health 17(8):2788. https://doi.org/10.3390/ijerph17082788

  18. Ding G, Qin L (2020) Study on the prediction of stock price based on the associated network model of LSTM. Int J Mach Learn Cybern 11(6):1307–1317. https://doi.org/10.1007/s13042-019-01041-1

    Article  Google Scholar 

  19. Zheng H, Lin F, Feng X et al (2020) A hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic flow prediction. IEEE Trans Intell Transp Syst 22(11):6910–6920. https://doi.org/10.1109/TITS.2020.2997352

    Article  Google Scholar 

  20. Lu W, Rui H, Liang C et al (2020) A method based on ga-cnn-lstm for daily tourist flow prediction at scenic spots. Entropy 22(3):261. https://doi.org/10.3390/e22030261

  21. Ma D, Song X, Li P (2020) Daily traffic flow forecasting through a contextual convolutional recurrent neural network modeling inter-and intra-day traffic patterns. IEEE Trans Intell Transp Syst 22(5):2627–2636. https://doi.org/10.1109/TITS.2020.2973279

    Article  Google Scholar 

  22. Kumar A, Islam T, Sekimoto Y et al. (2020) Convcast: An embedded convolutional LSTM based architecture for precipitation nowcasting using satellite data. Plos One 15(3):e0230114. https://doi.org/10.1371/journal.pone.0230114

  23. Poth CN, Bulut O, Aquilina AM et al (2021) Using data mining for rapid complex case study descriptions: Example of public health briefings during the onset of the COVID-19 pandemic. J Mixed Methods Res 15(3):348–373. https://doi.org/10.1177/15586898211013925

    Article  Google Scholar 

  24. Li L, Zhang Q, Wang X et al (2020) Characterizing the propagation of situational information in social media during covid-19 epidemic: A case study on weibo. IEEE Trans Comput Soc Syst 7(2):556–562. https://doi.org/10.1109/TCSS.2020.2980007

    Article  Google Scholar 

  25. Jia Q, Guo Y, Wang G et al. (2020) Big data analytics in the fight against major public health incidents (Including COVID-19): a conceptual framework. Int J Environ Res Public Health 17(17):6161. https://doi.org/10.3390/ijerph17176161

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Acknowledgements

The authors acknowledge the help from the university colleagues.

Funding

This research was supported by the project "Research on the dynamic evolution mechanism of network public opinion of public emergencies and the ability of local governments to cope with them" (Grant No. 21wsk169) (Wenzhou Philosophy and Social Science Planning in 2021).

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  1. Institute of Corporate Strategy and Corporate Culture, School of Business, Wenzhou University, Wenzhou, China

    Min Chen & Wenhu Du

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  1. Min Chen
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  2. Wenhu Du
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Correspondence to Min Chen.

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Chen, M., Du, W. The predicting public sentiment evolution on public emergencies under deep learning and internet of things. J Supercomput 79, 6452–6470 (2023). https://doi.org/10.1007/s11227-022-04900-x

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