A Real-time Visualization of Global Sentiment Analysis on Declaration of Pandemic

Main Article Content

Lavanya, A
Waqas Ali
Dr. Jaime Lloret
Vidya Sagar, S. D
Chivukula Bharadwaj

Abstract

The paper's objective is to carry out a real-time visualization of pandemic sentiment at the very first instance. The paper shows multilevel visualization of sentiment analysis conducted on the covid19 dataset acquired from Twitter. The visualization tools used for real-time data are Google data studio, Python matplotlib, Carto, and Tableau. On Mar 11, 2020, Covid19 was declared a global pandemic, and stage wise lockdown protocols were implemented. The covid19 virus has spread worldwide and consumed millions of people. The impact of the virus is affected not only on the physical body but also on mental health and results in increased distress, depression, anxiety, fear, and panic simultaneously. The data was downloaded using twitter's official API on Mar 11, 2020. Vader sentiment analysis is performed on 3,27,717 tweets downloaded from 200 Megacities globally. The study achieved 50.95% negative and 58.72% positive sentiment and neutral values ranging between 0 to 1 polarity.

Article Details

How to Cite
[1]
Lavanya, A, Waqas Ali, Dr. Jaime Lloret, Vidya Sagar, S. D, and Chivukula Bharadwaj, “A Real-time Visualization of Global Sentiment Analysis on Declaration of Pandemic”, Int. J. Comput. Eng. Res. Trends, vol. 9, no. 6, pp. 104–113, Jun. 2022.
Section
Research Articles

References

A Timeline of COVID-19 Developments in 2020. (2021, Jan 02). (ajmc.com) Retrieved Feb 24, 2021, from https://www.ajmc.com/view/a-timeline-of-covid19-developments-in-2020

Apricio, M., & Costa, C. J. (2015). Data visualization. Communication design quarterly review, 3(1), 7-11.

Barrett, P., Hunter, J., & Miller, J. T. (2005). Matplotlib--A Portable Python Plotting Package. Astronomical data analysis software and systems XIV, 347, 91.

Chppell, B. (2020, Maarch 11). Coronavirus: COVID-19 Is Now Officially A Pandemic, WHO Says. (npr.org) Retrieved Feb 24, 2021, from https://www.npr.org/sections/goatsandsoda/2020/03/11/814474930/coronavirus-covid-19-is-now-officially-a-pandemic-who-says

Day, T., Park, A., Madras, N., Gumel, A., & Wu, J. (2006). When is quarantine a useful control strategy for emerging infectious diseases? American journal of epidemiology, 163(5), 479-485.

Elbagir, S., & Yang, J. (2019). Twitter sentiment analysis using natural language toolkit and VADER sentiment. Proceedings of the International MultiConference of Engineers and Computer Scientists, 122, 16.

Elflein, J. (2021, feb 24). Covid-119 cases worldwide as of feb 24, 2021. (statista.com) Retrieved feb 24, 2021, from https://www.statista.com/statistics/1043366/novel-coronavirus-2019ncov-cases-worldwide-by-country/

Fernando, D. (2018). Visualisasi Data Menggunakan Google Data Studio. Prosiding Seminar Nasional Rekayasa Teknologi Informasi| SNARTISI.

Friendly, M. (2008). A brief history of data visualization. Handbook of data visualization, 15-56.

Ghebreysus, T. A. (2020). Addressing mental health needs: an integral part of COVID?19 response. World Psychiatry, 19(2), 129.

Hutto, C., & Gibert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media, 8(1).

Hyland, P., Shevlin, M., McBride, O., Murphy, J., & Karatzias, T. (2020). Anxiety and depression in the Republic of Ireland during the COVID?19 pandemic. Acta Psychiatrica Scandinavica, 142(3), 249-256.

Islam, M. A., Barna, S. D., Raihan, H., Khan, M. N., & Hossain, M. T. (2020). Depression and anxiety among university students during the COVID-19 pandemic. Bangladesh: A web-based cross-sectional survey PloS one, 15(8), e0238162.

Kim, A. E., Hansen, H. M., Murphy, J., Richard, A. K., Duke, J., & Allen, J. A. (2013). Methodological considerations in analyzing Twitter data. Journal of the National Cancer Institute Monographs, 2013(47), 140-146.

Lavanya, A., Panwar, D., Jaime, L., & et. al. (2022). Event-Based Multi-Model Classification to Assess the User Participation Levels on Twitter. In N. Thakur, & B. D. Parameshachari (Eds.), Human-Computer Interaction and Beyond: Advances Towards Smart and Interconnected Environments-II (pp. 76-120 (45)). Bentham Books.

Mane, S. B., Sawant, Y., Kazi, S., & Shinde, V. (2014). Real time sentiment analysis of twitter data using hadoop. International Journal of Computer Science and Information Technologies, 5(3), 3098-3100.

McGinty, E. E., Presskreischer, R., Han, H., & Barry, C. L. (2020). Psychological distress and loneliness reported by US adults in 2018 and April 2020. Jama, 324(1), 93-94.

Mucchetti, M. (2020). Google Data Studio. BigQuery for Data Warehousing, 401-416.

Murphy, S. A. (2013). Data visualization and rapid analytics: Applying tableau desktop to support library decision-making. Journal of Web Librarianship, 7(4), 465-476.

Ozdin, S., & Bayrak, O. S. (2020). Levels and predictors of anxiety, depression and health anxiety during COVID-19 pandemic. Turkish society: The importance of gender International Journal of Social Psychiatry, 66(5), 504-511.

Reece, A. G., Reagan, A. J., Lix, K. L., & Dodds, P. S. (2017). Forecasting the onset and course of mental illness with Twitter data. Scientific reports, 7(1), 1-11.

Roser, M. (2020, March 04). The Spanish flu (1918-20): The global impact of the largest influenza pandemic in history. Retrieved from ourworldindata.org: https://ourworldindata.org/spanish-flu-largest-influenza-pandemic-in-history

Sedoc, J., Buechel, S., Nachmany, Y., Buffone, A., & Ungar, L. (2019). Learning word ratings for empathy and distress from document-level user responses. arXiv preprint arXiv:1912.01079.

Talbot, J., Charron, V., & Konkle, A. (2021). Feeling the Void: Lack of Support for Isolation and Sleep Difficulties in Pregnant Women during the COVID-19 Pandemic Revealed by Twitter Data Analysis. International Journal of Environmental Research and Public Health, 18(2), 393.

Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol, 29(1), 24-54.

Telea, A. C. (2014). Data visualization: principles and practice. CRC Press.

VanderPlas. (2016). Python data science handbook: Essential tools for working with data. O'Reilly Media, Inc.

Wagh, B., Shinde, J. V., & Kale, P. A. (2018). A Twitter sentiment analysis using NLTK and machine learning techniques. International Journal of Emerging Research in Management and Technology, 6(12), 37-44.

Wang, C., Pan, R., Wan, X., & Tan, Y. (2020). A longitudinal study on the mental health of general population during the COVID-19 epidemic in China. Brain, behavior, and immunity, 87, 40-48.

Wesley, R., Eldridge, M., & Terlecki, P. T. (2001). An analytic data engine for visualization in tableau. Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, 1185-1194.

Zastrow, M. (2015). Data visualization: Science on the map. Nature News, 519(7541), 119.