Araştırma Makalesi

Twitter Sentiment Analysis During Covid-19 Outbreak with VADER

Cilt: 13 Sayı: 49 31 Mayıs 2022
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Twitter Sentiment Analysis During Covid-19 Outbreak with VADER

Öz

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.

Anahtar Kelimeler

Kaynakça

  1. Ahmed, M. E., Rabin, M. R. I., & Chowdhury, F. N. (2020). COVID-19: Social Media Sentiment Analysis on Reopening. arXiv preprint arXiv:2006.00804.
  2. 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
  3. C. Kaur and A. Sharma, (2020) “EasyChair Preprint Twitter Sentiment Analysis on Coronavirus using Textblob”.
  4. 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).
  5. 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.
  6. 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.
  7. 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.
  8. 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).

Ayrıntılar

Birincil Dil

İngilizce

Konular

-

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

31 Mayıs 2022

Gönderilme Tarihi

13 Şubat 2022

Kabul Tarihi

24 Nisan 2022

Yayımlandığı Sayı

Yıl 2022 Cilt: 13 Sayı: 49

Kaynak Göster

APA
Çılgın, C., Baş, M., Bilgehan, H., & Ünal, C. (2022). Twitter Sentiment Analysis During Covid-19 Outbreak with VADER. AJIT-e: Academic Journal of Information Technology, 13(49), 72-89. https://doi.org/10.5824/ajite.2022.02.001.x
AMA
1.Çılgın C, Baş M, Bilgehan H, Ünal C. Twitter Sentiment Analysis During Covid-19 Outbreak with VADER. AJIT-e. 2022;13(49):72-89. doi:10.5824/ajite.2022.02.001.x
Chicago
Çılgın, Cihan, Metin Baş, Hande Bilgehan, ve Ceyda Ünal. 2022. “Twitter Sentiment Analysis During Covid-19 Outbreak with VADER”. AJIT-e: Academic Journal of Information Technology 13 (49): 72-89. https://doi.org/10.5824/ajite.2022.02.001.x.
EndNote
Çılgın C, Baş M, Bilgehan H, Ünal C (01 Mayıs 2022) Twitter Sentiment Analysis During Covid-19 Outbreak with VADER. AJIT-e: Academic Journal of Information Technology 13 49 72–89.
IEEE
[1]C. Çılgın, M. Baş, H. Bilgehan, ve C. Ünal, “Twitter Sentiment Analysis During Covid-19 Outbreak with VADER”, AJIT-e, c. 13, sy 49, ss. 72–89, May. 2022, doi: 10.5824/ajite.2022.02.001.x.
ISNAD
Çılgın, Cihan - Baş, Metin - Bilgehan, Hande - Ünal, Ceyda. “Twitter Sentiment Analysis During Covid-19 Outbreak with VADER”. AJIT-e: Academic Journal of Information Technology 13/49 (01 Mayıs 2022): 72-89. https://doi.org/10.5824/ajite.2022.02.001.x.
JAMA
1.Çılgın C, Baş M, Bilgehan H, Ünal C. Twitter Sentiment Analysis During Covid-19 Outbreak with VADER. AJIT-e. 2022;13:72–89.
MLA
Çılgın, Cihan, vd. “Twitter Sentiment Analysis During Covid-19 Outbreak with VADER”. AJIT-e: Academic Journal of Information Technology, c. 13, sy 49, Mayıs 2022, ss. 72-89, doi:10.5824/ajite.2022.02.001.x.
Vancouver
1.Cihan Çılgın, Metin Baş, Hande Bilgehan, Ceyda Ünal. Twitter Sentiment Analysis During Covid-19 Outbreak with VADER. AJIT-e. 01 Mayıs 2022;13(49):72-89. doi:10.5824/ajite.2022.02.001.x