The crisis of educational network public opinion refers to issues such as public dissatisfaction and trust crisis caused by unexpected events in the field of education. The frequency and scope of the impact of educational network public opinion crisis are gradually expanding, bringing huge challenges to educational management departments and institutions. This article aimed to explore how to use deep learning technology to qualitatively predict and quantitatively analyze educational network public opinion crises. This article adopted experimental and comparative methods to qualitatively and quantitatively analyze the crisis of public opinion in educational networks, and obtained the characteristics of several deep learning methods. Experimental data showed that under the framework of convolutional neural network (CNN), the accuracy of public opinion crisis prediction in the sigmoid function was 99.9%; the loss rate was 0.34%, and the running time was 65 seconds.

Na Wei. Qualitative Prediction and Quantitative Analysis of Events in Educational Network Public Opinion Crisis: a Deep Learning Approach. Frontiers in Educational Psychology (2023), Vol. 4, Issue 1: 52-61. https://doi.org/10.38007/JEP.2023.040106.

[1] Andy Ohemeng Asare, Robin Yap, Ngoc Truong, Eric Ohemeng Sarpong:The pandemic semesters: Examining public opinion regarding online learning amidst COVID-19. J. Comput. Assist. Learn. 37(6): 1591-1605 (2021)

[2] Andreas Giannakoulopoulos, Alexandros Kouretsis, Laida Limniati:Tracking the public's opinion of online education: a quantitative analysis of tweets on e-learning. Int. J. Learn. Technol. 14(4): 271-287 (2019)

[3] Ying Qu, Hong Chen:Research on endogenous internet public opinion dissemination in Chinese universities based on SNIDR model. J. Intell. Fuzzy Syst. 44(6): 9901-9917 (2023)

[4] Robert Luzsa, Susanne Mayr:Links Between Users' Online Social Network Homogeneity, Ambiguity Tolerance, and Estimated Public Support for Own Opinions. Cyberpsychology Behav. Soc. Netw. 22(5): 325-329 (2019)

[5] Xing Zhang, Yan Zhou, Fuli Zhou, Saurabh Pratap:Internet public opinion dissemination mechanism of COVID-19: evidence from the Shuanghuanglian event. Data Technol. Appl. 56(2): 283-302 (2022)

[6] Qingqing Li, Ziming Zeng, Shouqiang Sun, Chen Cheng, Yingqi Zeng:Constructing a spatiotemporal situational awareness framework to sense the dynamic evolution of online public opinion on social media. Electron. Libr. 41(5): 722-749 (2023)

[7] Yucheng Dong, Zhaogang Ding, Francisco Chiclana, Enrique Herrera-Viedma:Dynamics of Public Opinions in an Online and Offline Social Network. IEEE Trans. Big Data 7(4): 610-618 (2021)

[8] Aarushi Vohra, Ritu Garg:Deep learning based sentiment analysis of public perception of working from home through tweets. J. Intell. Inf. Syst. 60(1): 255-274 (2023)

[9] Geetanjali Sahi:Public Sentiment on Ayodhya Verdict by the Supreme Court of India: A Temporal Analysis on Twitter Data. Int. J. Inf. Commun. Technol. Hum. Dev. 14(1): 1-17 (2022)

[10] Dwijendra Nath Dwivedi, Ghanashyama Mahanty, Anilkumar Vemareddy:How Responsible Is AI?: Identification of Key Public Concerns Using Sentiment Analysis and Topic Modeling. Int. J. Inf. Retr. Res. 12(1): 1-14 (2022)

[11] Abdullah Hussein Alamoodi, Mohammed Rashad Baker, Osamah Shihab Albahri, Bilal Bahaa Zaidan, A. A. Zaidan, Wing-Kwong Wong, Salem Garfan, Ahmed Shihab Albahri, Miguel A. Alonso, Ali Najm Jasim, M. J. Baqer:Public Sentiment Analysis and Topic Modeling Regarding COVID-19's Three Waves of Total Lockdown: A Case Study on Movement Control Order in Malaysia. KSII Trans. Internet Inf. Syst. 16(7): 2169-2190 (2022)

[12] Tanya Malhotra, Anjana Gupta:A systematic review of developments in the 2-tuple linguistic model and its applications in decision analysis. Soft Comput. 27(4): 1871-1905 (2023)

[13] Asad Abdi, Gayane Sedrakyan, Bernard P. Veldkamp, Jos van Hillegersberg, Stéphanie M. van den Berg:Students feedback analysis model using deep learning-based method and linguistic knowledge for intelligent educational systems. Soft Comput. 27(19): 14073-14094 (2023)

[14] Markus Binder, Bernd Heinrich, Marcus Hopf, Alexander Schiller:Global reconstruction of language models with linguistic rules - Explainable AI for online consumer reviews. Electron. Mark. 32(4): 2123-2138 (2022)

[15] Nabila Amir, Fouzia Jabeen, Zafar Ali, Irfan Ullah, Asim Ullah Jan, Pavlos Kefalas:On the current state of deep learning for news recommendation. Artif. Intell. Rev. 56(2): 1101-1144 (2023)

[16] Jaan Aru, Aqeel Labash, Oriol Corcoll, Raul Vicente:Mind the gap: challenges of deep learning approaches to Theory of Mind. Artif. Intell. Rev. 56(9): 9141-9156 (2023)

[17] Kodati Dheeraj, Ramakrishnudu Tene:Identifying suicidal emotions on social media through transformer-based deep learning. Appl. Intell. 53(10): 11885-11917 (2023)

[18] Andrea Hrckova, Róbert Móro, Ivan Srba, Mária Bieliková:Quantitative and qualitative analysis of linking patterns of mainstream and partisan online news media in Central Europe. Online Inf. Rev. 46(5): 954-973 (2022)

[19] Ivan Heibi, Silvio Peroni:A quantitative and qualitative open citation analysis of retracted articles in the humanities. Quant. Sci. Stud. 3(4): 953-975 (2022)

[20] Irvin Dongo, Yudith Cardinale, Ana Isabel Aguilera, Fabiola Martínez, Yuni Quintero, Germán Robayo, David Cabeza:A qualitative and quantitative comparison between Web scraping and API methods for Twitter credibility analysis. Int. J. Web Inf. Syst. 17(6): 580-606 (2021)