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Year 2021, Volume: 4 Issue: 3, 302 - 311, 31.12.2021

Buket Kaya Abdullah Günay

https://doi.org/10.35377/saucis...932620
Cited By: 2

Abstract

References

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  • [2] Ipsos, Covid-19 Dönemi ve Evdeki Keşifler Accessed August 20,2020. https://www.ipsos.com/sites/default/files/ipsossia_trends_6nisan2020.pdf
  • [3] Murthy, D., Twitter. Cambridge: Polity Press, 2018.
  • [4] Szomszor, M., Kostkova, P., & De Quincey, E., # Swineflu: Twitter predicts swine flu outbreak in 2009. In International conference on electronic healthcare (pp. 18-26). Springer, Berlin, Heidelberg, 2010.
  • [5] Bian, J., Topaloglu, U., & Yu, F. Towards large-scale twitter mining for drug-related adverse events. In Proceedings of the 2012 international workshop on Smart health and wellbeing (pp. 25-32), 2012.
  • [6] Nguyen, L. T., Wu, P., Chan, W., Peng, W., & Zhang, Y. Predicting collective sentiment dynamics from time-series social media. In Proceedings of the first international workshop on issues of sentiment discovery and opinion mining (pp. 1-8), 2012.
  • [7] Claster, W. B., Dinh, H., & Cooper, M. Naïve Bayes and unsupervised artificial neural nets for Cancun tourism social media data analysis. In 2010 Second World Congress on Nature and Biologically Inspired Computing (NaBIC) (pp. 158-163). IEEE, 2010.
  • [8] Pang, B., Lee, L., & Vaithyanathan, S. Thumbs up? Sentiment classification using machine learning techniques. arXiv preprint cs/0205070, 2002.
  • [9] Tong, R. M. An operational system for detecting and tracking opinions in on-line discussion. In Working Notes of the ACM SIGIR 2001 Workshop on Operational Text Classification (Vol. 1, No. 6), 2001.
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  • [11] Tuzcu, S. Çevrimiçi Kullanıcı Yorumlarının Duygu Analizi ile Sınıflandırılması. Eskişehir Türk Dünyası Uygulama ve Araştırma Merkezi Bilişim Dergisi, 1(2), 1-5, 2020.
  • [12] Sabuncu, İ., & Atmis, M. SOCIAL MEDIA ANALYTICS FOR BRAND IMAGE TRACKING: A CASE STUDY APPLICATION FOR TURKISH AIRLINES. Yönetim Bilişim Sistemleri Dergisi, 6(1), 26-41, 2020.
  • [13] Kılıç, G., Budak, İ., & Kılıç, B. S. Kara Cuma Etiketlerinin Tweet İstatistikleri ve Duygu Analizi ile Sıralanması. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 23(1), 131-140, 2020.
  • [14] Kilimci, Z. H. Financial sentiment analysis with Deep Ensemble Models (DEMs) for stock market prediction. Journal of the Faculty of Engineering and Architecture of Gazi University, 35(2), 635-650, 2020.
  • [15] Küçükkartal, H. K. Twitter'daki Verilere Metin Madenciliği Yöntemlerinin Uygulanması. Eskişehir Türk Dünyası Uygulama ve Araştırma Merkezi Bilişim Dergisi, 1(2), 10-13, 2020.
  • [16] Buğra, A. Y. A. N., Kuyumcu, B., & Ceylan, B. Twitter Üzerindeki İslamofobik Twitlerin Duygu Analizi ile Tespiti. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 7(2), 495-502, 2019.
  • [17] Can., M., & Gürsoy, U. T. SOSYAL MEDYA ETKİNLİĞİNİN ÖLÇÜMÜ: FİRMALARIN TWITTER KULLANIMINA İLİŞKİN BİR İNCELEME. Bolu Abant İzzet Baysal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 20(1), 121-146, 2020.
  • [18] Samuel, J., Ali, G. G., Rahman, M., Esawi, E., & Samuel, Y. Covid-19 public sentiment insights and machine learning for tweets classification. Information, 11(6), 314, 2020.
  • [19] Chen, F., Yuan, Z., & Huang, Y. Multi-source data fusion for aspect-level sentiment classification. Knowledge-Based Systems, 187, 104831, 2020.
  • [20] Lu, Q., Zhu, Z., Xu, F., Zhang, D., Wu, W., & Guo, Q. Bi-GRU Sentiment Classification for Chinese Based on Grammar Rules and BERT. International Journal of Computational Intelligence Systems, 13(1), 538-548, 2020.
  • [21] Wang, T., Lu, K., Chow, K. P., & Zhu, Q. COVID-19 Sensing: Negative sentiment analysis on social media in China via Bert Model. IEEE Access, 8, 138162-138169, 2020.
  • [22] Li, W., Wu, H., Zhu, N., Jiang, Y., Tan, J., & Guo, Y. Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU). Information Processing in Agriculture, 8(1), 185-193, 2021.
  • [23] Trstenjak, B., Mikac, S., & Donko, D. KNN with TF-IDF based framework for text categorization. Procedia Engineering, 69, 1356-1364, 2014.
  • [24] Bengio, Y., Goodfellow, I., & Courville, A. Deep learning (Vol. 1). Massachusetts, USA:: MIT press, 2017.
  • [25] Basiri, M. E., Nemati, S., Abdar, M., Cambria, E., & Acharya, U. R. ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis. Future Generation Computer Systems, 115, 279-294, 2021.

Twitter Sentiment Analysis Based on Daily Covid-19 Table in Turkey

Year 2021, Volume: 4 Issue: 3, 302 - 311, 31.12.2021

Buket Kaya Abdullah Günay

https://doi.org/10.35377/saucis...932620
Cited By: 2

Abstract

The coronavirus epidemic, which began to affect the whole world in early 2020, has become the most talked about agenda item by individuals. Individuals announce their feelings and thoughts through various communication channels and receive news from what is happening around them. One of the most important channels of communication is Twitter. Individuals express their feelings and thoughts by interacting with the tweets posted. The aim of this study is to analyze the emotions of the comments made under the "daily coronavirus table" shared by the Republic of Turkey Ministry of Health and to measure their relationship with the daily number of cases and deaths. In the study, emotional classification of tweets was implemented using LSTM, GRU and BERT methods from deep learning algorithms, and the results of all three algorithms were compared with the daily number of cases and deaths.

Keywords

deep Learnig NLP LSTM BERT GRU sentiment analysis Covid19

References

  • [1] Kemp, S., “Digital 2020: 3.8 Billion People Use Social Media.”, January 30, 2020. https://wearesocial.com/blog/2020/01/digital-2020-3-8-billion-people-use-social-media
  • [2] Ipsos, Covid-19 Dönemi ve Evdeki Keşifler Accessed August 20,2020. https://www.ipsos.com/sites/default/files/ipsossia_trends_6nisan2020.pdf
  • [3] Murthy, D., Twitter. Cambridge: Polity Press, 2018.
  • [4] Szomszor, M., Kostkova, P., & De Quincey, E., # Swineflu: Twitter predicts swine flu outbreak in 2009. In International conference on electronic healthcare (pp. 18-26). Springer, Berlin, Heidelberg, 2010.
  • [5] Bian, J., Topaloglu, U., & Yu, F. Towards large-scale twitter mining for drug-related adverse events. In Proceedings of the 2012 international workshop on Smart health and wellbeing (pp. 25-32), 2012.
  • [6] Nguyen, L. T., Wu, P., Chan, W., Peng, W., & Zhang, Y. Predicting collective sentiment dynamics from time-series social media. In Proceedings of the first international workshop on issues of sentiment discovery and opinion mining (pp. 1-8), 2012.
  • [7] Claster, W. B., Dinh, H., & Cooper, M. Naïve Bayes and unsupervised artificial neural nets for Cancun tourism social media data analysis. In 2010 Second World Congress on Nature and Biologically Inspired Computing (NaBIC) (pp. 158-163). IEEE, 2010.
  • [8] Pang, B., Lee, L., & Vaithyanathan, S. Thumbs up? Sentiment classification using machine learning techniques. arXiv preprint cs/0205070, 2002.
  • [9] Tong, R. M. An operational system for detecting and tracking opinions in on-line discussion. In Working Notes of the ACM SIGIR 2001 Workshop on Operational Text Classification (Vol. 1, No. 6), 2001.
  • [10] Özyurt, B., & Akçayol, M. A. FİKİR MADENCİLİĞİ VE DUYGU ANALİZİ, YAKLAŞIMLAR, YÖNTEMLER ÜZERİNE BİR ARAŞTIRMA. Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi, 6(4), 668-693, 2018.
  • [11] Tuzcu, S. Çevrimiçi Kullanıcı Yorumlarının Duygu Analizi ile Sınıflandırılması. Eskişehir Türk Dünyası Uygulama ve Araştırma Merkezi Bilişim Dergisi, 1(2), 1-5, 2020.
  • [12] Sabuncu, İ., & Atmis, M. SOCIAL MEDIA ANALYTICS FOR BRAND IMAGE TRACKING: A CASE STUDY APPLICATION FOR TURKISH AIRLINES. Yönetim Bilişim Sistemleri Dergisi, 6(1), 26-41, 2020.
  • [13] Kılıç, G., Budak, İ., & Kılıç, B. S. Kara Cuma Etiketlerinin Tweet İstatistikleri ve Duygu Analizi ile Sıralanması. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 23(1), 131-140, 2020.
  • [14] Kilimci, Z. H. Financial sentiment analysis with Deep Ensemble Models (DEMs) for stock market prediction. Journal of the Faculty of Engineering and Architecture of Gazi University, 35(2), 635-650, 2020.
  • [15] Küçükkartal, H. K. Twitter'daki Verilere Metin Madenciliği Yöntemlerinin Uygulanması. Eskişehir Türk Dünyası Uygulama ve Araştırma Merkezi Bilişim Dergisi, 1(2), 10-13, 2020.
  • [16] Buğra, A. Y. A. N., Kuyumcu, B., & Ceylan, B. Twitter Üzerindeki İslamofobik Twitlerin Duygu Analizi ile Tespiti. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 7(2), 495-502, 2019.
  • [17] Can., M., & Gürsoy, U. T. SOSYAL MEDYA ETKİNLİĞİNİN ÖLÇÜMÜ: FİRMALARIN TWITTER KULLANIMINA İLİŞKİN BİR İNCELEME. Bolu Abant İzzet Baysal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 20(1), 121-146, 2020.
  • [18] Samuel, J., Ali, G. G., Rahman, M., Esawi, E., & Samuel, Y. Covid-19 public sentiment insights and machine learning for tweets classification. Information, 11(6), 314, 2020.
  • [19] Chen, F., Yuan, Z., & Huang, Y. Multi-source data fusion for aspect-level sentiment classification. Knowledge-Based Systems, 187, 104831, 2020.
  • [20] Lu, Q., Zhu, Z., Xu, F., Zhang, D., Wu, W., & Guo, Q. Bi-GRU Sentiment Classification for Chinese Based on Grammar Rules and BERT. International Journal of Computational Intelligence Systems, 13(1), 538-548, 2020.
  • [21] Wang, T., Lu, K., Chow, K. P., & Zhu, Q. COVID-19 Sensing: Negative sentiment analysis on social media in China via Bert Model. IEEE Access, 8, 138162-138169, 2020.
  • [22] Li, W., Wu, H., Zhu, N., Jiang, Y., Tan, J., & Guo, Y. Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU). Information Processing in Agriculture, 8(1), 185-193, 2021.
  • [23] Trstenjak, B., Mikac, S., & Donko, D. KNN with TF-IDF based framework for text categorization. Procedia Engineering, 69, 1356-1364, 2014.
  • [24] Bengio, Y., Goodfellow, I., & Courville, A. Deep learning (Vol. 1). Massachusetts, USA:: MIT press, 2017.
  • [25] Basiri, M. E., Nemati, S., Abdar, M., Cambria, E., & Acharya, U. R. ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis. Future Generation Computer Systems, 115, 279-294, 2021.
There are 25 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Articles
Authors

Buket Kaya 0000-0001-9505-181X

Abdullah Günay 0000-0002-5006-1448

Publication Date December 31, 2021
Submission Date May 4, 2021
Acceptance Date November 3, 2021
Published in Issue Year 2021Volume: 4 Issue: 3

Cite

IEEE B. Kaya and A. Günay, “Twitter Sentiment Analysis Based on Daily Covid-19 Table in Turkey”, SAUCIS, vol. 4, no. 3, pp. 302–311, 2021, doi: 10.35377/saucis...932620.

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