Abstract
Education has been severely impacted by the spread of the COVID-19 virus. In order to prevent the spread of the COVID-19 virus and maintain education in the current climate, governments have compelled the public to adopt online platforms. Consequently, this decision has affected numerous lives in various ways. To investigate the impact of COVID-19 on students’ education, we amassed a dataset consisting of 10,000 tweets. The motivations of the study are; (i) to analyze the positive, negative, and neutral effects of COVID-19 on education; (ii) to analyze the opinions of stakeholders in their tweets about the transition from formal education to e-learning; (iii) to analyze people’s feelings and reactions to these changes; and (iv) to analyze the effects of different training methods on different groups. We constructed emotion recognition models utilizing shallow and deep techniques, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long-short Term Memory (LSTM), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and Logical Regression (LR). RF algorithms with a bag-of-words model outperformed with over 80% accuracy in recognizing emotions.
This research is supported by Istanbul Kultur University under ULEP-2022–2023.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
available at https://github.com/twintproject/twint.
- 2.
available at https://jcharis.github.io/neattext.
- 3.
available at https://www.anaconda.com/state-of-data-science-2020.
References
Althagafi, A.H., Althobaiti, G., Alhakami, H., Alsubait, T.: Arabic tweets sentiment analysis about online learning during Covid-19 in Saudi Arabia. Int. J. Adv. Comput. Sci. Appl. 12, 620–625 (2021)
Ayaz, T.B., et al.: Global impact of the pandemic on education: a study of natural language processing. In: 2022 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1–4. IEEE (2022)
Bhagat, K.K., Mishra, S., Dixit, A., Chang, C.Y.: Public opinions about online learning during covid-19: a sentiment analysis approach. Sustainability 13(6) (2021). https://doi.org/10.3390/su13063346, https://www.mdpi.com/2071-1050/13/6/3346
Bird, S., Loper, E.: Nltk: the natural language toolkit. Association for Computational Linguistics (2004)
Blanco, G., Lourenço, A.: Optimism and pessimism analysis using deep learning on covid-19 related twitter conversations. Inf. Process. Manag. 59(3), 102918 (2022). https://doi.org/10.1016/j.ipm.2022.102918, https://www.sciencedirect.com/science/article/pii/S0306457322000437
Dorman-Ilan, S., et al.: Anxiety and depression symptoms in covid-19 isolated patients and in their relatives. Front. Psychiatry 11, 581598 (2020)
Dorn, E., Hancock, B., Sarakatsannis, J., Viruleg, E.: Covid-19 and education: the lingering effects of unfinished learning. McKinsey Comp. 27, 1–15 (2021)
Hutto, C., Gilbert, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8, pp. 216–225 (2014)
Loria, S., et al.: Textblob documentation. Release 0.15 2, 269 (2018)
Mathur, A., Kubde, P., Vaidya, S.: Emotional analysis using twitter data during pandemic situation: covid-19. In: 2020 5th International Conference on Communication and Electronics Systems (ICCES), pp. 845–848 (2020). https://doi.org/10.1109/ICCES48766.2020.9138079
Miks, J., McIlwaine, J.: Keeping the world’s children learning through covid-19. Acesso em 6(05) (2020)
Mujahid, M., et al.: Sentiment analysis and topic modeling on tweets about online education during covid-19. Appl. Sci. 11(18), 8438 (2021). https://doi.org/10.3390/app11188438
Mushtaque, I., Rizwan, M., Dasti, R.K., Ahmad, R., Mushtaq, M.: Students’ attitude and impact of online learning; role of teachers and classmate support during the covid-19 crisis. Perf. Improv. 60(5), 20–27 (2021)
Okango, E., Mwambi, H.: Dictionary based global twitter sentiment analysis of coronavirus (COVID-19) effects and response. Ann. Data Sci. 9, 1–12 (2021). https://doi.org/10.1007/s40745-021-00358-5
Priyadarshini, I., Mohanty, P., Kumar, R., Sharma, R., Puri, V., Singh, P.K.: A study on the sentiments and psychology of twitter users during COVID-19 lockdown period. Multimedia Tools Appl., 1–23 (2021). https://doi.org/10.1007/s11042-021-11004-w
Relucio, F.S., Palaoag, T.D.: Sentiment analysis on educational posts from social media. In: Proceedings of the 9th International Conference on E-Education, E-Business, E-Management and E-Learning, IC4E 2018, pp. 99–102. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3183586.3183604
Sadigov, R., Yıldırım, E., Kocaçınar, B., Patlar Akbulut, F., Catal, C.: Deep learning-based user experience evaluation in distance learning. Cluster Comput., 1–13 (2023)
Sosun, S.D., et al.: Deep sentiment analysis with data augmentation in distance education during the pandemic. In: 2022 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1–5. IEEE (2022)
Sunitha, D., Patra, R.K., Babu, N., Suresh, A., Gupta, S.C.: Twitter sentiment analysis using ensemble based deep learning model towards covid-19 in India and European countries. Pattern Recogn. Lett. 158, 164–170 (2022). https://doi.org/10.1016/j.patrec.2022.04.027, https://www.sciencedirect.com/science/article/pii/S0167865522001246
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 IFIP International Federation for Information Processing
About this paper
Cite this paper
Kocaçınar, B., Qarizada, N., Dikkaya, C., Azgun, E., Yıldırım, E., Akbulut, F.P. (2023). Analysis of the Lingering Effects of Covid-19 on Distance Education. In: Maglogiannis, I., Iliadis, L., MacIntyre, J., Dominguez, M. (eds) Artificial Intelligence Applications and Innovations. AIAI 2023. IFIP Advances in Information and Communication Technology, vol 675. Springer, Cham. https://doi.org/10.1007/978-3-031-34111-3_17
Download citation
DOI: https://doi.org/10.1007/978-3-031-34111-3_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34110-6
Online ISBN: 978-3-031-34111-3
eBook Packages: Computer ScienceComputer Science (R0)
