Research Article
BibTex RIS Cite

COVID-19 Salgını Sürecinde Uzaktan Eğitime İlişkin Tweetlerin Duygusal Analizi

Year 2021, Volume: 9 Issue: 18, 853 - 868, 21.12.2021
https://doi.org/10.18009/jcer.950790

Abstract

Sosyal medya ortamları insanların duygu ve düşüncelerini ifade ettikleri popüler platformlar haline gelmiştir. Twitter bu platformların başında gelmektedir. Twitter günümüzde önemli bir veri kaynağına dönüşmüş ve farklı alanlarda duygu analizi çalışmalarında rol oynamıştır. Bu çalışmada covid-19 sürecinde uzaktan eğitime ilişkin atılan tweetler üzerinde duygu analizi çalışması yapılmıştır. Veri seti olarak Kaggle veri paylaşım platformu üzerinden açık erişimle sunulan veri seti kullanılmıştır. Bu veri setinden rastgele alınan 999 kayıt el yordamı ile pozitif veya negatif olarak etiketlenmiştir. KNIME üzerinde duygu analizi modeli kurulmuştur. Etiketlenen bu veri seti KNIME ile uygun düğümler kullanılarak önce ön işleme ile analize hazır hale getirilmiş, daha sonra duygusal analiz aşamalarından geçirilerek, çıktı için başarı hesaplaması yapılmıştır. Sözlük tabanlı yaklaşımın esas alındığı çalışmada %88.4 doğruluk oranına ulaşıldığı görülmüştür.

References

  • Akın, B., & Şimşek, U.T.G. (2018). Sosyal medya analitiği ile değer yaratma: duygu analizi ile geleceğe yönelim. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 5(3), 797-811.
  • Albayrak, M., Topal, K., & Altıntaş, V. (2017). Sosyal medya üzerinde veri analizi: Twitter. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22 (Kayfor 15 Özel Sayısı), 1991-1998.
  • Altunay, M.C. (2010). Gündelik yaşam ve sosyal paylaşım ağları: Twitter ya da"pıt pıt net". Galatasaray Üniversitesi İletişim Dergisi, 12, 31-56.
  • Aramaki, E., Maskawa, S., & Morita, M. (2011, July). Twitter catches the flu: detecting influenza epidemics using Twitter. In Proceedings of the 2011 Conference on empirical methods in natural language processing (pp. 1568-1576).
  • Ayan, B, Kuyumcu, B, Ceylan, B. (2019). Twitter üzerindeki islamofobik twitlerin duygu nalizi ile tespiti. Gazi University Journal of Science Part C: Design and Technology, 7(2), 495-502. DOI: 10.29109/gujsc.561806
  • Berthold, M. R., Cebron, N., Dill, F., Gabriel, T. R., Kötter, T., Meinl, T., ... & Wiswedel, B. (2009). KNIME-the Konstanz information miner: version 2.0 and beyond. ACM SIGKDD Explorations Newsletter, 11(1), 26-31.
  • Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8.
  • Boon-Itt, S., & Skunkan, Y. (2020). Public perception of the COVID-19 pandemic on Twitter: sentiment analysis and topic modeling study. JMIR Public Health and Surveillance, 6(4), e21978.
  • Buzzi, M. C., Buzzi, M., & Leporini, B. (2011). Web 2.0: Twitter and the blind. In Proceedings of the 9th ACM SIGCHI Italian Chapter International Conference on Computer-Human Interaction: Facing Complexity (pp. 151-156), ACM. (2011, September).
  • Caelen, O. (2017). A Bayesian interpretation of the confusion matrix. Annals of Mathematics and Artificial Intelligence, 81(3), 429-450.
  • Flach, P. (2019). Performance evaluation in machine learning: The good, the bad, the ugly, and the way forward. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 9808-9814).
  • Garcia, K., & Berton, L. (2021). Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA. Applied Soft Computing, 101, 107057.
  • Greenhow, C., Lewin, C., & Staudt Willet, K. B. (2020). The educational response to Covid-19 across two countries: a critical examination of initial digital pedagogy adoption. Technology, Pedagogy and Education, 1-19.
  • Hoque, M. N., Coelho, D., & Mueller, K (2019). Examining the visualization practices of data scientists on Kaggle, IEEE VIS 2019, 20-25 October, Vancouver, BC, Canada.
  • İlhan, N., & Sağaltıcı, D. (2020) Twitter'da duygu analizi. Harran Üniversitesi Mühendislik Dergisi, 5(2), 146-156. Joshi, M., Das, D., Gimpel, K., & Smith, N. A. (2010, June). Movie reviews and revenues: An experiment in text regression. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics (pp. 293-296).
  • Maas, A., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011). Learning word vectors for sentiment analysis. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies (pp. 142-150).
  • Mahmud, J., Nichols, J., & Drews, C. (2014). Home location identification of twitter users. ACM Transactions on Intelligent Systems and Technology (TIST), 5(3), 47.
  • Meral, M,, & Diri, B. (2014, Nisan) “Twitter üzerinde duygu analizi”. IEEE 22. Sinyal İşleme ve İletişim Uygulamaları Kurultayı, Trabzon, Türkiye.
  • Nemes, L., & Kiss, A. (2021). Social media sentiment analysis based on COVID-19. Journal of Information and Telecommunication, 5(1), 1-15.
  • Onan, A. (2017). Twitter mesajları üzerinde makine öğrenmesi yöntemlerine dayalı duygu analizi. Yönetim Bilişim Sistemleri Dergisi, 3(2), 1-14.
  • Özyurt, B., & Akçayol, M. A. (2018). Fikir madenciliği ve duygu analizi, yaklaşımlar, yöntemler üzerine bir araştırma. Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi, 6(4), 668-693.
  • Paudel, P. (2021). Online education: Benefits, challenges and strategies during and after COVID-19 in higher education. International Journal on Studies in Education, 3(2), 70-85.
  • Peker, M. (2017). Yeni bir veri önişleme metodu: k-harmonik kümeleme tabanlı öznitelik ağırlıklandırma. D.Ü. Mühendislik Fakültesi Mühendislik Dergisi, 8(4), 767-779.
  • Rizun, M., & Strzelecki, A. (2020). Students’ acceptance of the COVID-19 impact on shifting higher education to distance learning in Poland. International Journal of Environmental Research and Public Health, 17(18), 64-68.
  • Sahayak, V., Shete, V., & Pathan, A. (2015). Sentiment analysis on twitter data. International Journal of Innovative Research in Advanced Engineering (IJIRAE), 2(1), 178-183.
  • Sarıman, G., & Mutaf, E. (2020). COVID-19 sürecinde twitter mesajlarının duygu analizi. Euroasia Journal of Mathematics, Engineering, Natural & Medical Sciences, 7(10), 137-148.
  • Szomszor, M., Kostkova, P., & De Quincey, E. (2010, December). # Swineflu: Twitter predicts swine flu outbreak in 2009. In International conference on electronic healthcare (pp. 18-26). Springer, Berlin, Heidelberg.
  • Toquero, C. M. (2021). Emergency remote education experiment amid COVID-19 pandemic. International Journal of Educational Research and Innovation, 15, 162-176.
  • Uçan, A. (2014). Otomatik duygu sözlüğü çevirimi ve duygu analizinde kullanımı, Yayımlanmamış Yüksek Lisans Tezi, Hacettepe Üniversitesi, Fen Bilimleri Enstitüsü, Ankara.
  • World Health Organization (WHO). (2020). Weekly epidemiological update: Coronavirus disease 2019 (COVID-19). WHO. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports
  • Xue, J., Chen, J., Chen, C., Zheng, C., Li, S., & Zhu, T. (2020). Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter. PloS one, 15(9), e0239441.

Sentiment Analysis of Tweets Relating to Distance Education during the Covid-19 Pandemic

Year 2021, Volume: 9 Issue: 18, 853 - 868, 21.12.2021
https://doi.org/10.18009/jcer.950790

Abstract

Social media environments have become popular platforms where people express their feelings and thoughts. Twitter is one of these platforms. Today, Twitter has become an important data source and has played a role in sentiment analysis studies in different fields. In this study, sentiment analysis was conducted on tweets about distance education during the covid-19 process. As the data set, the data set provided with open access over the Kaggle data sharing platform was used. 999 records randomly retrieved from this dataset were manually labeled as positive or negative. A sentiment analysis model was established on KNIME. This labeled data set was first prepared for analysis by preprocessing by using the appropriate nodes with KNIME, and then passed through the emotional analysis stages and a success calculation was made for the output. In the study, which was based on the dictionary-based approach, it was seen that the accuracy rate was 88.4%.

References

  • Akın, B., & Şimşek, U.T.G. (2018). Sosyal medya analitiği ile değer yaratma: duygu analizi ile geleceğe yönelim. Mehmet Akif Ersoy Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 5(3), 797-811.
  • Albayrak, M., Topal, K., & Altıntaş, V. (2017). Sosyal medya üzerinde veri analizi: Twitter. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 22 (Kayfor 15 Özel Sayısı), 1991-1998.
  • Altunay, M.C. (2010). Gündelik yaşam ve sosyal paylaşım ağları: Twitter ya da"pıt pıt net". Galatasaray Üniversitesi İletişim Dergisi, 12, 31-56.
  • Aramaki, E., Maskawa, S., & Morita, M. (2011, July). Twitter catches the flu: detecting influenza epidemics using Twitter. In Proceedings of the 2011 Conference on empirical methods in natural language processing (pp. 1568-1576).
  • Ayan, B, Kuyumcu, B, Ceylan, B. (2019). Twitter üzerindeki islamofobik twitlerin duygu nalizi ile tespiti. Gazi University Journal of Science Part C: Design and Technology, 7(2), 495-502. DOI: 10.29109/gujsc.561806
  • Berthold, M. R., Cebron, N., Dill, F., Gabriel, T. R., Kötter, T., Meinl, T., ... & Wiswedel, B. (2009). KNIME-the Konstanz information miner: version 2.0 and beyond. ACM SIGKDD Explorations Newsletter, 11(1), 26-31.
  • Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1-8.
  • Boon-Itt, S., & Skunkan, Y. (2020). Public perception of the COVID-19 pandemic on Twitter: sentiment analysis and topic modeling study. JMIR Public Health and Surveillance, 6(4), e21978.
  • Buzzi, M. C., Buzzi, M., & Leporini, B. (2011). Web 2.0: Twitter and the blind. In Proceedings of the 9th ACM SIGCHI Italian Chapter International Conference on Computer-Human Interaction: Facing Complexity (pp. 151-156), ACM. (2011, September).
  • Caelen, O. (2017). A Bayesian interpretation of the confusion matrix. Annals of Mathematics and Artificial Intelligence, 81(3), 429-450.
  • Flach, P. (2019). Performance evaluation in machine learning: The good, the bad, the ugly, and the way forward. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 9808-9814).
  • Garcia, K., & Berton, L. (2021). Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA. Applied Soft Computing, 101, 107057.
  • Greenhow, C., Lewin, C., & Staudt Willet, K. B. (2020). The educational response to Covid-19 across two countries: a critical examination of initial digital pedagogy adoption. Technology, Pedagogy and Education, 1-19.
  • Hoque, M. N., Coelho, D., & Mueller, K (2019). Examining the visualization practices of data scientists on Kaggle, IEEE VIS 2019, 20-25 October, Vancouver, BC, Canada.
  • İlhan, N., & Sağaltıcı, D. (2020) Twitter'da duygu analizi. Harran Üniversitesi Mühendislik Dergisi, 5(2), 146-156. Joshi, M., Das, D., Gimpel, K., & Smith, N. A. (2010, June). Movie reviews and revenues: An experiment in text regression. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics (pp. 293-296).
  • Maas, A., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011). Learning word vectors for sentiment analysis. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies (pp. 142-150).
  • Mahmud, J., Nichols, J., & Drews, C. (2014). Home location identification of twitter users. ACM Transactions on Intelligent Systems and Technology (TIST), 5(3), 47.
  • Meral, M,, & Diri, B. (2014, Nisan) “Twitter üzerinde duygu analizi”. IEEE 22. Sinyal İşleme ve İletişim Uygulamaları Kurultayı, Trabzon, Türkiye.
  • Nemes, L., & Kiss, A. (2021). Social media sentiment analysis based on COVID-19. Journal of Information and Telecommunication, 5(1), 1-15.
  • Onan, A. (2017). Twitter mesajları üzerinde makine öğrenmesi yöntemlerine dayalı duygu analizi. Yönetim Bilişim Sistemleri Dergisi, 3(2), 1-14.
  • Özyurt, B., & Akçayol, M. A. (2018). Fikir madenciliği ve duygu analizi, yaklaşımlar, yöntemler üzerine bir araştırma. Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi, 6(4), 668-693.
  • Paudel, P. (2021). Online education: Benefits, challenges and strategies during and after COVID-19 in higher education. International Journal on Studies in Education, 3(2), 70-85.
  • Peker, M. (2017). Yeni bir veri önişleme metodu: k-harmonik kümeleme tabanlı öznitelik ağırlıklandırma. D.Ü. Mühendislik Fakültesi Mühendislik Dergisi, 8(4), 767-779.
  • Rizun, M., & Strzelecki, A. (2020). Students’ acceptance of the COVID-19 impact on shifting higher education to distance learning in Poland. International Journal of Environmental Research and Public Health, 17(18), 64-68.
  • Sahayak, V., Shete, V., & Pathan, A. (2015). Sentiment analysis on twitter data. International Journal of Innovative Research in Advanced Engineering (IJIRAE), 2(1), 178-183.
  • Sarıman, G., & Mutaf, E. (2020). COVID-19 sürecinde twitter mesajlarının duygu analizi. Euroasia Journal of Mathematics, Engineering, Natural & Medical Sciences, 7(10), 137-148.
  • Szomszor, M., Kostkova, P., & De Quincey, E. (2010, December). # Swineflu: Twitter predicts swine flu outbreak in 2009. In International conference on electronic healthcare (pp. 18-26). Springer, Berlin, Heidelberg.
  • Toquero, C. M. (2021). Emergency remote education experiment amid COVID-19 pandemic. International Journal of Educational Research and Innovation, 15, 162-176.
  • Uçan, A. (2014). Otomatik duygu sözlüğü çevirimi ve duygu analizinde kullanımı, Yayımlanmamış Yüksek Lisans Tezi, Hacettepe Üniversitesi, Fen Bilimleri Enstitüsü, Ankara.
  • World Health Organization (WHO). (2020). Weekly epidemiological update: Coronavirus disease 2019 (COVID-19). WHO. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports
  • Xue, J., Chen, J., Chen, C., Zheng, C., Li, S., & Zhu, T. (2020). Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter. PloS one, 15(9), e0239441.
There are 31 citations in total.

Details

Primary Language Turkish
Subjects Other Fields of Education
Journal Section Research Article
Authors

Özcan Özyurt 0000-0002-0047-6813

Nilgün Kısa 0000-0001-9330-481X

Publication Date December 21, 2021
Submission Date June 10, 2021
Acceptance Date August 15, 2021
Published in Issue Year 2021 Volume: 9 Issue: 18

Cite

APA Özyurt, Ö., & Kısa, N. (2021). COVID-19 Salgını Sürecinde Uzaktan Eğitime İlişkin Tweetlerin Duygusal Analizi. Journal of Computer and Education Research, 9(18), 853-868. https://doi.org/10.18009/jcer.950790

download13894               13896   13897 14842      


Creative Commons License


This work is licensed under a Creative Commons Attribution 4.0 International License.


Dear Authors;

We would like to inform you that ORCID, which includes 16 digit number will be requested from the authors for the studies to be published in JCER. It is important to be sensitive on this issue. 


Best regards...