COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020. Therefore, it is the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyzes two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from 1st Jan 2019 to 23rd March 2020 have been analyzed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226,668 tweets collected within the time span between December 2019 and May 2020 have been analyzed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%.

Department of Computer Science and Engineering CHRIST Bangalore India

Department of Computer Science and Engineering Supreme Knowledge Foundation Group of Institutions Mankundu India

Department of Information Systems College of Computer Science and Information Technology King Faisal University Saudi Arabia

Faculty of Computers and Artificial Intelligence Cairo University Egypt

Faculty of Electrical Engineering and Computer Science VSB Technical University of Ostrava Czech Republic

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Internet Users Worldwide Statistic, Available at: https://www.broadbandsearch.net/blog/internet-statistics, Anonymous, retrieved 10th September, 2020.

Belli B. 2020. Yale webinars: Using emotional intelligence to combat COVID-19 anxiety. Available at: https://news.yale.edu/2020/03/24/yale-webinars-using-emotional-intelligence-combat-covid-19-anxiety.

Schultz F., Utz S., Göritz A. Is the medium the message? Perceptions of and reactions to crisis communication via twitter, blogs and traditional media. Publ. Relat. Rev. 2011;37(1):20–27. Mar 1.

FÅ. Nielsen, A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. arXiv preprint arXiv:1103.2903. 15 Mar.

Garain Avishek. English language tweets dataset for COVID-19. IEEE Dataport. 2020 doi: 10.21227/2zg4-yx02. DOI

https://www.tweetbinder.com/blog/covid-19-coronavirus-twitter/, Anonymous, Retrieved on 8th May, 2020.

Chakraborty K., Bhattacharyya S., Bag R. A survey of sentiment analysis from social media data. IEEE Trans. Comput. Soc. Syst. 2020;7(2):450–464. Jan 7.

Madaan R., Bhatia K.K., Bhatia S. 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) IEEE; 2020. Understanding the role of emotional intelligence in usage of social media; pp. 586–591. Jan 29.

HuiD S.I.A., Madani T.A., Ntoumi F., Koch R., Dar O. The continuing 2019-nCoV epidemic threat of novel corona viruses to global health: the latest 2019 novel coronavirus outbreak in Wuhan, China. Int. J. Infect. Dis. 2020;91(2020):264–266. PubMed PMC

Süral I., Griffiths M.D., Kircaburun K., Emirtekin E. Trait emotional intelligence and problematic social media use among adults: The mediating role of social media use motives. Int. J. Mental Health Addict. 2019;17(2):336–345. Apr 15;

O. Hornung, S. Dittes, S. Smolnik, When Emotions go Social–Understanding the Role of Emotional Intelligence in Social Network use, Research-in-Progress Papers, 40, https://aisel.aisnet.org/ecis2018_rip/40.

Herodotou C., Kambouri M., Winters N. The role of trait emotional intelligence in gamers’ preferences for play and frequency of gaming. Comput. Human Behav. 2011;27(5):1815–1819. Sep 1.

Chen S.C., Lin C.P. Understanding the effect of social media marketing activities: The mediation of social identification, perceived value, and satisfaction. Technol. Forecast. Soc. Change. 2019;140:22–32. Mar 1.

Kim J.W., Chock T.M. Personality traits and psychological motivations predicting selfie posting behaviors on social networking sites. Telemat. Inform. 2017;34(5):560–571.

Depoux A., Martin S., Karafillakis E., Preet R., Wilder-Smith A., Larson H. The pandemic of social media panic travels faster than the COVID-19 outbreak. J. Travel Med. 2020;27(3):taaa031. doi: 10.1093/jtm/taaa031. PubMed DOI PMC

Merchant R.M., Lurie N. Social media and emergency preparedness in response to novel coronavirus. JAMA. 2020;323(20):2011–2012. doi: 10.1001/jama.2020.4469. PubMed DOI

Z. Hu, Z. Yang, Q. Li, A. Zhang, Y. Huang, Infodemiological study on COVID-19 epidemic and COVID-19 infodemic. Preprints. 2020 Mar 4.

Li S., Wang Y., Xue J., Zhao N., Zhu T. The impact of COVID-19 epidemic declaration on psychological consequences: a study on active weibo users. Int. J. Environ. Res. Publ. Health. 2020;17(6):2032. Jan. PubMed PMC

Liu M., Xue J., Zhao N., Wang X., Jiao D., Zhu T. Using social media to explore the consequences of domestic violence on mental health. J. Interpers. Viol. 2018 Feb 1:0886260518757756. PubMed

Alhajji M., Al Khalifah A., Aljubran M., Alkhalifah M. 2020. Sentiment analysis of tweets in Saudi Arabia regarding governmental preventive measures to contain COVID-19. Preprints. DOI

Talwar S., Dhir A., Kaur P., Zafar N., Alrasheedy M. Why do people share fake news? Associations between the dark side of social media use and fake news sharing behavior. J. Retail. Consumer Serv. 2019;51:72–82. Nov 1.

K. Sharma, S. Seo, C. Meng, S. Rambhatla, A. Dua, Y. Liu, Coronavirus on social media: Analyzing misinformation in Twitter conversations. arXiv preprint arXiv:2003.12309 2020 Mar 26.

Ghafarian S.H., Yazdi H.S. Identifying crisis-related informative tweets using learning on distributions. Inform. Process. Manage. 2020;57(2) Mar 1.

Wilder-Smith A., Freedman D.O. Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. J. Travel Med. 2020;27(2):taaa020. Mar. PubMed PMC

Larson H.J. The biggest pandemic risk? Viral misinformation. Nature. 2018;562(7726):309–310. Oct 1. PubMed

Van Bavel J.J., Baicker K., Boggio P.S., Capraro V., Cichocka A., Cikara M., Crockett M.J., Crum A.J., Douglas K.M., Druckman J.N., Drury J. Using social and behavioural science to support COVID-19 pandemic response. Nat. Hum. Behav. 2020;30:1–2. PubMed

Dur-e Ahmad M., Imran M. Transmission dynamics model of coronavirus COVID-19 for the outbreak in most affected countries of the world. Int. J. Interact. Multimedia Artif. Intell. 2020;6(2):7–10.

Wang Y., McKee M., Torbica A., Stuckler D. Systematic literature review on the spread of health-related misinformation on social media. Soc. Sci. Med. 2019;18 Sep. PubMed PMC

Pennycook G., McPhetres J., Zhang Y., Lu J.G., Rand D.G. Fighting COVID-19 misinformation on social media: Experimental evidence for a scalable accuracy-nudge intervention. Psychol. Sci. 2020 0956797620939054. PubMed PMC

Latif Siddique, Usman Muhammad, Manzoor Sanaullah, Iqbal Waleed, Qadir Junaid, Tyson Gareth. 2020. Leveraging data science to combat COVID-19: A comprehensive review. TechRxiv. Preprint. PubMed DOI PMC

Saiz F., Barandiaran I. COVID-19 detection in chest x-ray images using a deep learning approach. Int. J. Interact. Multimedia Artif. Intell. 2020;6(Regular Issue):4. doi: 10.9781/ijimai.2020.04.003. DOI

Samuel J., Ali G.G.M.N., Rahman M.M., Esawi E., Samuel Y. COVID-19 public sentiment insights and machine learning for tweets classification. Information. 2020;11:314.

N2-Barkur G., Vibha, Kamath G.B. Sentiment analysis of nationwide lockdown due to COVID 19 outbreak: Evidence from India. Asian J. Psychiatry. 2020;51 doi: 10.1016/j.ajp.2020.102089. Advance online publication. PubMed DOI PMC

Binti Hamzah F.A., Lau C., Nazri H., Ligot D.V., Lee G., Tan C.L. Coronatracker: Worldwide COVID-19 outbreak data analysis and prediction. [preprint] Bull World Health Organ. E-pub. 2020;19 doi: 10.2471/BLT.20.255695. DOI

Abd-Alrazaq A., Alhuwail D., Househ M., Hamdi M. Top concerns of tweeters during the COVID-19 pandemic: Infoveillance study. J. Med. Internet Res. 2020;22(4) e19016DOI. PubMed PMC

Li S., Wang Y., Xue J., Zhao N., Zhu T. The impact of COVID-19 epidemic declaration on psychological consequences: a study on active weibo users. Int. J. Environ. Res. Publ. Health. 2020;17(6):2032. PubMed PMC

F.Å. Nielsen. 2011. A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. arXiv preprint arXiv:1103.2903.

E. Kouloumpis, T. Wilson, J. Moore, Twitter sentiment analysis: The good the bad and the omg!, in: Fifth International AAAI conference on weblogs and social media. (2011, July).

https://en.wikipedia.org/wiki/Fuzzy_logic, Anonymous, retrieved 8th August, 2020.

Sadollah Ali, (October 31st 2018). Introductory Chapter: Which membership function is appropriate in fuzzy system?, Fuzzy Logic Based in Optimization Methods and Control Systems and Its Applications, Ali Sadollah, IntechOpen, 10.5772/intechopen.79552. DOI

Hameed I.A. Using Gaussian membership functions for improving the reliability and robustness of students’ evaluation systems. Expert Syst. Appl. 2011;38(6):7135–7142.

Mamdani E.H., Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Mach. Stud. 1975;7(1):1–13.

Wang K. Agile Manufacturing: The 21st Century Competitive Strategy, vol. 29. 2001. Computational intelligence in agile manufacturing engineering; pp. 7–315. DOI

Ghani U., Bajwa I.S., Ashfaq A. A fuzzy logic based intelligent system for measuring customer loyalty and decision making. Symmetry. 2018;10:761.

Eshan S.C., Hasan M.S. 2017 20th International Conference of Computer and Information Technology (ICCIT), December. IEEE; 2017. An application of machine learning to detect abusive bengali text; pp. 1–6.

Varghese R., Jayasree M. 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), August. IEEE; 2013. Aspect based sentiment analysis using support vector machine classifier; pp. 1581–1586.

Q. Le, T. Mikolov, (2014, January). Distributed representations of sentences and documents, in: International Conference on Machine Learning, pp. 1188-1196.

Rokach L. Data Mining and Knowledge Discovery Handbook. Springer; Boston, MA: 2005. Ensemble methods for classifiers; pp. 957–980.

P. Gamallo, M. Garcia, Citius: A naivebayes strategy for sentiment analysis on english tweets, in: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) (2014, August).

I. Rish, An empirical study of the naive Bayes classifier, in: IJCAI 2001 workshop on empirical methods in artificial intelligence, Vol. 3(22) (2001, August), pp. 41-46.

Tyagi Abhilasha, Sharma Naresh. Sentiment analysis using logistic regression and effective word score heuristic. Int. J. Eng. Technol. (UAE) 2018;7:20–23. doi: 10.14419/ijet.v7i2.24.11991. DOI

S. Al-Azani, E.S.M. El-Alfy, (2017, January). Using word embedding and ensemble learning for highly imbalanced data sentiment analysis in short arabic text, in: ANT/SEIT, pp. 359-366.

Fushiki T. Estimation of prediction error by using K-fold cross-validation. Stat. Comput. 2011;21(2):137–146.

Pete Blair J., Rossmo D.K. Evidence in context: Bayes’ theorem and investigations. Police Quart. 2010;13(2):123–135. doi: 10.1177/1098611110365686. DOI

https://en.wikipedia.org/wiki/Naive_Bayes_classifier, Anonymous, retrieved 12th September, 2020.

Rokach L. Ensemble-based classifiers. Artif. Intell. Rev. 2010;33(1–2):1–39.

Mikolov T., Sutskever I., Chen K., Corrado G.S., Dean J. Distributed representations of words and phrases and their compositionality. Adv. Neural Inform. Process. Syst. 2013 3111-3119.

Da Silva N.F.F., Hruschka E.R., Hruschka E.R. Tweet sentiment analysis with classifier ensembles. Decis. Support Syst. 2014;66:170–179. doi: 10.1016/j.dss.2014.07.003. DOI

Bilgin M., Şentürk İ.F. 2017 International Conference on Computer Science and Engineering (UBMK) October. IEEE; 2017. Sentiment analysis on twitter data with semi-supervised doc2vec; pp. 661–666.

M.M. Truşcă, (2019, October). Efficiency of SVM classifier with Word2Vec and Doc2Vec models, in: Proceedings of the International Conference on Applied Statistics, Vol. 1(1), pp. 496-503, Sciendo.

Olsen C., St George D.M.M. Cross-sectional study design and data analysis. College Entrance Exam. Board. 2004;26(03):2006.

Sharma R., Mondal D., Bhattacharyya P. A comparison among significance tests and other feature building methods for sentiment analysis: A first study. In: Gelbukh A., editor. Comput. Linguist. Intell. Text Process. Springer International Publishing; Cham: 2018. pp. 3–19.

Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme