Covid-19 Salgını Esnasında VADER ile Twitter Duygu Analizi
Avrupa'yı etkisi altına aldığından beri Covid-19 salgını, özellikle Amerika kıtasında hızla yayılmaya devam etmektedir. Güncel verilere bakıldığında virüs yaklaşık 250 milyon insanı etkilemiş ve beş milyondan fazla insanın ölümüne neden olmuştur. Özellikle Avrupa kıtasında salgının hızla yayılmasıyla birlikte bu konu sosyal medyada tartışılmaya başlanmıştır. Özellikle Twitter bu çalışma alanında en sık kullanılan mikroblogdur. Bu çalışmada, küresel COVID-19 salgını sırasında Twitter üzerinden birçok kişi, kuruluş ve devlet kurumu tarafından paylaşılan tweetlerin VADER Duygu Analizi yöntemi kullanılarak, duygu analizi gerçekleştirilmesi amaçlanmaktadır. Bu çalışmada #covid19, #Covid, #pandemic, #social-distance, #socialdistance, #covid-19, #corona-virius, #coronavirus, #Chinesevirus, #Chinese-virus hashtagleri kullanılmıştır. Bu hashtag'ler ile 1 Ocak 2020 ile 1 Temmuz 2020 tarihleri arasında Twitter'dan toplam 60.243.040 tweet toplanmıştır. Bu çalışmada, Covid-19 ile ilgili Twitter verilerinde ifade edilen duyguları sınıflandırmak için VADER kullanılmış ve ortaya çıkan tweetlerin bileşik puanları, çok olumlu, olumlu, nötr, olumsuz, çok olumsuz olmak üzere beş kategoriye ayrılmıştır. Ayrıca çalışmada, aylık olarak en sık toplanan metin verilerinin görselleştirilmesi için Wordcloud kullanılmış ve tweetlerin içeriğini daha iyi anlamak için tweetlere N-gram uygulanmıştır. Çalışmada elde edilen sonuçlar incelendiğinde, çıkışın farklı dönemlerinde Covid-19 ile ilgili paylaşılan tweetlerin farklı duygusal durumları yansıtması oldukça ilginçtir.
Anahtar Kelimeler:
Covid-19, Koronavirüs, Duygu Analizi, VADER, Twitter
Twitter Sentiment Analysis During Covid-19 Outbreak with VADER
The Covid-19 outbreak, which has been under the influence of Europe since then, continues to spread rapidly especially in the American continent. Looking at the current data, the virus has affected about 250 million people and has killed more than five million people. Especially with the rapid spread of the outbreak in the European continent, this issue started to be discussed in social media. In particular, Twitter is the most frequently used micro-blogging in this workspace. In this study, it is aimed to analyze the tweets shared by many people, organizations and government agencies through Twitter during the global COVID-19 outbreak with sentiment analysis using the VADER Sentiment Analysis method. The hashtags #covid19, #Covid, #pandemic, #social-distancing, #socialdistance, #covid-19, #corona-virius, #coronavirus, #Chinesevirus, #Chinese-virus were used in this study. With these hashtags, a total of 60,243,040 tweets were collected from Twitter between January 1, 2020 and July 1, 2020. In this study, we use the VADER to classify the sentiments expressed in Twitter data related to Covid-19 and the compound scores of the resulting tweets were divided into five categories: Highly Positive, Positive, Neutral, Negative, Highly Negative. In addition, in the study, the Wordcloud was used to visualize the most frequently collected text data monthly, and N-grams were applied to the tweets to better understand the content of the tweets. When the results obtained in the study are examined, it is quite interesting that the tweets shared about Covid-19 in different periods of the release reflect different sentimental situations.
Keywords:
Covid-19, Coronavirus, Sentiment Analysis, VADER, Twitter,
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Ahmed, M. E., Rabin, M. R. I., & Chowdhury, F. N. (2020). COVID-19: Social Media Sentiment Analysis on Reopening. arXiv preprint arXiv:2006.00804.Andrade, Francisca Marli Rodrigues de and Barreto, Tarssio Brito and Herrera-Feligreras, Andrés and Ugolini, Andrea and Lu, Yu-Ting, (2020). Twitter in Brazil: Discourses on China in Times of Coronavirus. Available at SSRN: https://ssrn.com/abstract=3608566 or http://dx.doi.org/10.2139/ssrn.3608566
C. Kaur and A. Sharma, (2020) “EasyChair Preprint Twitter Sentiment Analysis on Coronavirus using Textblob”.
Cavnar, W. B., & Trenkle, J. M. (1994, April). N-gram-based text categorization. In Proceedings of SDAIR-94, 3rd annual symposium on document analysis and information retrieval (Vol. 161175).
Chauhan, V. K., Bansal, A., & Goel, A. (2018). Twitter sentiment analysis using vader. International Journal of Advance Research, Ideas and Innovations in Technology (IJARIIT), 4(1), 485-489.
Chew, C., & Eysenbach, G. (2010). Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak. PloS one, 5(11), e14118.
Dubey, A. D. (2020). Decoding the Twitter Sentiments towards the Leadership in the times of COVID-19: A Case of USA and India. Available at SSRN 3588623.
Elbagir, S., & Yang, J. (2019). Twitter Sentiment Analysis Using Natural Language Toolkit and VADER Sentiment. In Proceedings of the International MultiConference of Engineers and Computer Scientists (pp. 122-16).
Fung, I. C. H., Fu, K. W., Ying, Y., Schaible, B., Hao, Y., Chan, C. H., & Tse, Z. T. H. (2013). Chinese social media reaction to the MERS-CoV and avian influenza A (H7N9) outbreaks. Infectious diseases of poverty, 2(1), 31.
Hutto, C. J., & Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Eighth international AAAI conference on weblogs and social media.
Kim, E. H. J., Jeong, Y. K., Kim, Y., Kang, K. Y., & Song, M. (2016). Topic-based content and sentiment analysis of Ebola virus on Twitter and in the news. Journal of Information Science, 42(6), 763-781.
Kruspe, A., Haeberle, M., & Zhu, X. X. (2020). Cross-language sentiment analysis of European Twitter messages during the COVID-19 pandemic McNeill, A., Harris, P. R., & Briggs, P. (2016). Twitter influence on UK vaccination and antiviral uptake during the 2009 H1N1 pandemic. Frontiers in public health, 4, 26.
Pokharel, B. P. (2020). Twitter Sentiment analysis during COVID-19 Outbreak in Nepal. Available at SSRN: https://ssrn.com/abstract=3624719 or http://dx.doi.org/10.2139/ssrn.3624719
Prabhakar Kaila, D., & Prasad, D. A. (2020). Informational flow on Twitter–Corona virus outbreak–topic modelling approach. International Journal of Advanced Research in Engineering and Technology (IJARET), 11(3).
Ramteke, J., Shah, S., Godhia, D., & Shaikh, A. (2016, August). Election result prediction using Twitter sentiment analysis. In 2016 international conference on inventive computation technologies (ICICT) (1), 1-5. IEEE.
Shin, S. Y., Seo, D. W., An, J., Kwak, H., Kim, S. H., Gwack, J., & Jo, M. W. (2016). High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea. Scientific reports, 6, 32920.
Van Lent, L. G., Sungur, H., Kunneman, F. A., Van De Velde, B., & Das, E. (2017). Too far to care? Measuring public attention and fear for Ebola using Twitter. Journal of medical Internet research, 19(6), e193.
Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., ... & Zhao, Y. (2020). Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. Jama, 323(11), 1061-1069.
World Health Organization (WHO). “WHO Coronavirus Disease (COVID-19) Dashboard”. Available at https://covid19.who.int/?gclid=Cj0KCQjw0rr4BRCtARIsAB0_48O8MNIGaRGaCcjx RCLkiPW 6kidbaM4Fb_xGsU9E7HOnqrjtx8_bLogaAgKVEALw_wcB
World Health Organization. (2020). Coronavirus disease 2019 (COVID-19) situation report–51. Geneva, Switzerland: World Health Organization. https://www.who.int/docs/default-source/coronaviruse/situationreports/20200311-sitrep-51-covid-19.pdf?sfvrsn=1ba62e57_10
Zheng, Y. Y., Ma, Y. T., Zhang, J. Y., & Xie, X. (2020). COVID-19 and the cardiovascular system. Nature Reviews Cardiology, 17(5), 259-260.
Zhou, P., Yang, X. L., Wang, X. G., Hu, B., Zhang, L., Zhang, W., ... & Chen, H. D. (2020). A pneumonia outbreak associated with a new coronavirus of probable bat origin. nature, 579(7798), 270-273.
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