Covid19 Sürecinde Çevrimiçi Eğitim Hakkındaki Toplum Görüşlerinin İncelenmesi: Sentiment Analizi

Year 2021, , 425 - 431, 01.12.2021

Cansu Aydın

https://doi.org/10.31590/ejosat.1035267

Cited By: 1

Abstract

Dünya Sağlık Örgütü'nün (WHO) Mart 2020'de Covid19 salgınını ilan etmesiyle başlayan pandemi süreci, pek çok sektörü olduğu gibi eğitim sektörünü de benzeri görülmemiş bir şekilde etkiledi. Covid19 nedeniyle yaşanan karantina döneminde insanlar duygularını ifade etmek ve kendilerini sakinleştirmenin bir yolunu bulmak için sosyal ağları her zamankinden daha fazla kullandılar. Günümüzde sosyal medya platformları insanların günlük yaşamları için ve politika gündemini belirlemede büyük önem taşımaktadır (Wu ve diğerleri, 2013). Özellikle pandemi dönemi ile birlikte çevrimiçi öğrenmenin artan yaygınlığı ve çevrimiçi öğrenme ile ilgili düzenli olarak sosyal medyada yer alan çok sayıda haber dikkate alındığında, Covid19 Salgını sırasında halkın çevrimiçi eğitime ilişkin görüşlerini öğrenmek için sosyal medya veri kaynaklarını kullanarak duygu analizi yöntemi kullanılmıştır. Veri kaynağı olarak Twitter seçilmiş ve Tweepy kütüphanesi kullanılarak metin madenciliği yapılmıştır. Koronavirüs ve uzaktan eğitimle ilgili gerekli hashtag'ler kullanılarak yalnızca İngilizce tweet'ler veri setinde yer almıştır. Toplanan veriler 03-05-2021 ile 31-05-2021 tarihleri arasındaki 5 haftaya aittir. Duygu analizi sonuçları ile toplumun çevrimiçi öğrenme konusundaki memnuniyetsizliği, beğenisi ve kaygıları yönetim tarafından hızlı bir şekilde öğrenilebilmesi ve eğitim ve öğretim hizmetlerinin kalitesinin artırılmasına yönelik stratejiler geliştirilmesi mümkündür. Bu çalışmada , yapılan duygu analizi sonuçları paylaşılmıştır.

Keywords

covid-19, duygu analizi, çevrimiçi eğitim, sentiment analizi

Examining Public Opinion Regarding Online Learning during Covid19 Outbreak: Sentiment Analysis

Abstract

The pandemic process, which started with the World Health Organization (WHO) declaring a Covid19 epidemic in March 2020, has affected the education sector in an unprecedented way, as it has many other sectors (World Health Organization, 2020). During the quarantine period due to Covid19, people have used social networks more than ever to express their feelings and find a way to calm themselves. Today, social media platforms are of great importance for their daily lives and in setting policy agenda (Wu et all, 2013). Considering the increasing prevalence of online learning and a large number of items that regularly appear about online learning on social media, especially with the pandemic period, sentiment analysis was used as a method to learn the opinions of the public on online education during Covid19 Outbreak. Twitter has been chosen as a data source and text mining has been conducted using Tweepy library. Only English tweets were mined using necessary hashtags related to coronavirus and distance learning. The collected data is 5 weeks from 03-05-2021 to 31-05-2021. With the results of sentiment analysis, it is possible to quickly learn the dissatisfaction, appreciation and concerns of the society about online learning by the management and to develop strategies to increase the quality of education and training services. In this study, the results of the sentiment analysis are provided.

Keywords

covid-19, sentiment analysis, online learning, emotion analysis

References

• Altawaier, M. M., & Tiun, S. (2016). Comparison of machine learning approaches on arabic twitter sentiment analysis. International Journal on Advanced Science, Engineering and Information Technology, 6(6), 1067-1073.
• Asare, A. O., Yap, R., Truong, N., & Sarpong, E. O.(2020) The pandemic semesters: Examining public opinion regarding online learning amidst COVID‐19. Journal of Computer Assisted Learning.
• Bounabi, M., Moutaouakil, K. E., & Satori, K. (2019). A comparison of text classification methods using different stemming techniques. International Journal of Computer Applications in Technology, 60(4), 298-306.
• Chakraborty, P., Mittal, P., Gupta, M. S., Yadav, S., & Arora, A. (2021). Opinion of students on online education during the COVID‐19 pandemic. Human Behavior and Emerging Technologies, 3(3), 357-365.
• Charles-Smith, L. E., Reynolds, T. L., Cameron, M. A., Conway, M., Lau, E. H., Olsen, J. M., & Corley, C. D. (2015). Using social media for actionable disease surveillance and outbreak management: a systematic literature review. PloS one, 10(10), e0139701.
• Chamlertwat, W., Bhattarakosol, P., Rungkasiri, T., & Haruechaiyasak, C. (2012). Discovering Consumer Insight from Twitter via Sentiment Analysis. J. Univers. Comput. Sci., 18(8), 973-992.
• Drias, H. H., & Drias, Y. (2020). Mining Twitter Data on COVID-19 for Sentiment analysis and frequent patterns Discovery. medRxiv. Dubey, "Twitter Sentiment Analysis during COVID-19 Outbreak", 2020.
• Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012-1014.
• Feezell, J. T. (2018). Agenda setting through social media: The importance of incidental news exposure and social filtering in the digital era. Political Research Quarterly, 71(2), 482-494.
• Krouska, A., Troussas, C., & Virvou, M. (2016, July). The effect of preprocessing techniques on Twitter sentiment analysis. In 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-5). IEEE.
• Manguri, K. H., Ramadhan, R. N., & Amin, P. R. M. (2020). Twitter sentiment analysis on worldwide COVID-19 outbreaks. Kurdistan Journal of Applied Research, 54-65.
• Loria, S. (2018). textblob Documentation. Release 0.15, 2, 269.
• Man Hung, Evelyn Lauren, Eric S Hon, Wendy C Birmingham, Julie Xu, Sharon Su, Shirley D Hon, Jungweon Park, Peter Dang, Martin S Lipsky. Originally published in the Journal of Medical
• Mourad, A. Srour, H. Harmanani, C. Jenainatiy and M. Arafeh, "Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions", Computer Science, 2020.
• Medhat, W.; Hassan, A.; Korashy, H. Sentiment analysis algorithms and applications: A survey. Ain Shams Eng. J. 2014, 5, 1093–1113.
• Nasukawa, T. and J. Yi, 2003. Sentiment analysis: Capturing favorability using natural languageprocessing. Proceedings of the 2nd InternationalConference on Knowledge Capture, Oct. 23-25, ACM, Sanibel Island, FL, USA, pp: 70-77.
• Nartiningrum, N., & Nugroho, A. (2020). Online learning amidst global pandemic: EFL students’ challenges, suggestions, and needed materials. ENGLISH FRANCA: Academic Journal of English Language and Education, 4(2), 115-140.
• Nhlabano, V. V., & Lutu, P. E. N. (2018, August). Impact of text pre-processing on the performance of sentiment analysis models for social media data. In 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD) (pp. 1-6). IEEE.
• Onyenwe, I., Nwagbo, S., Mbeledogu, N., & Onyedinma, E. (2020). The impact of political party/candidate on the election results from a sentiment analysis perspective using# AnambraDecides2017 tweets. Social Network Analysis and Mining, 10(1), 1-17. Pak, A., & Paroubek, P. (2010, May). Twitter as a corpus for sentiment analysis and opinion mining. In LREc (Vol. 10, No. 2010, pp. 1320-1326).
• Protess, D., & McCombs, M. E. (2016). Agenda setting: Readings on media, public opinion, and policymaking. Routledge.
• Pecar, S., Simko, M., & Bielikova, M. (2018, August). Sentiment analysis of customer reviews: Impact of text pre-processing. In 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA) (pp. 251-256). IEEE.
• Persada, S., Oktavianto, A., Miraja, B., Nadlifatin, R., Belgiawan, P., & Redi, A. P. (2020). Public Perceptions of Online Learning in Developing Countries: A Study Using The ELK Stack for Sentiment Analysis on Twitter. International Journal of Emerging Technologies in Learning (iJET), 15(9), 94-109.
• Shofiya, C., & Abidi, S. (2021). Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data. International Journal of Environmental Research and Public Health, 18(11), 5993.
• World Health Organization. Coronavirus disease 2019 (COVID-19) situation report. 2020.
• Wu, Y., Atkin, D., Lau, T. Y., Lin, C., & Mou, Y. (2013). Agenda setting and micro-blog use: An analysis of the relationship between Sina Weibo and newspaper agendas in China. The Journal of Social Media in Society, 2(2).
• Yaqub, U., Sharma, N., Pabreja, R., Chun, S. A., Atluri, V., & Vaidya, J. (2018, May). Analysis and visualization of subjectivity and polarity of Twitter location data. In Proceedings of the 19th annual international conference on digital government research: governance in the data age (pp. 1-10).