Abstract
In this crisis of COVID19, everyone is staying in touch with the world through social media. This has led to social media becoming a significant source of new information for many people and unfortunately this phenomenon has given birth to a lot of misinformation, chaos and fear in people’s minds. This fear is often due to the inadequate and wrong information. Therefore, there is a important need to understand this crisis. Patterns need to be established between popular tweets and its effect on the public’s sentiments, especially their fear. So, tweets of three different countries namely United States of America, Federative Republic of Brazil and Republic of India. Sentiment analysis reveals that fear of this unknown and mysterious nature of the coronavirus is dominant among the public. Predominant analysis of tweets within past two months will be done and then a model will be built to predict future reaction of the general public based on the crisis level in the country. Machine Learning algorithms such as ‘Logistic Regression (LR)’, ‘Multinomial Naïve Bayes’ and ‘Support Vector Machine (SVM)’are used for classification purpose preceded by the pre-processing steps of raw data from each country. 90% of accuracy has been achieved from sentiment classification result. Insights to the fear, sentiments have also been provided. Tweets with negative sentiment and emotion indicates the cases for the pandemic outbreak.
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References
J. Samuel, G.G.M. Nawaz Ali, M.M. Rahaman, E. Esawi, Y. Samuel: COVID 19 Public sentiment insights and machine learning for tweets classification. Information 11(6), 1–3 (2020)
K.H. Manguri, R.N. Ramadhan, P. Rasul, M. Amin, Twitter sentiment analysis on worldwide COVID-19 outbreaks. Kurdistan J. Appl. Res. 5, 54–55 (2020)
N. Aguilar-Gallegos, L.E. Romero-García, E.G. Martínez-González, E.I. García-Sánchez, J. Aguilar-Ávila, Datasets on dynamics of coronavirus on Twitter. Data Brief 30, 105684 (2020)
C. Ordun, S. Purosatham, E. Raff, Exploratory analysis of COVID-19 tweets using topic modelling, UMAP and Di-graphs (arXiv, 2020), pp. 1–2
R.J. Medford, S.N. Saleh, A. Sumarsono, T.M. Perl, C.U.J. Lehmann, An “infodemic”: leveraging high-volume twitter data to understand public sentiment for the COVID-19 outbreak. Kurdistan J. Appl. Res. 7(7), 2–3 (2020)
M. Alhajji, A.A. Khalifah, M. Aljubran, M. Alkhalifah, Sentiment analysis of Tweets in Saudi Arabia regarding governmental preventive measures to contain COVID-19 (Preprints, 2020), pp. 1–5
C.K.L.S. Pastor, Sentiment analysis of Filipinos and effects of extreme community quarantine due to Coronavirus (COVID-19) pandemic. J. Crit. Rev. 7, 2–4 (2020)
N.K. Rajput, B.A. Grover, V.K.J. Rathi, Word frequency and sentiment analysis of Twitter messages during Coronavirus pandemic (arXiv, 2020), pp. 1–3
M. Ra, B. Ab, S. Kc, COVID-19 outbreak: tweet based analysis and visualization towards the influence of coronavirus in the world. Gedrag Organisatie Rev. 33(02), 8–9 (2020)
A. D. Dubey, Twitter sentiment analysis during COVID19 outbreak. Kurdistan J. Appl. Res., 5–6 (2020)
J. Zhang, Y. Yang, Robustness of regularized linear classification methods in text categorization, in Proceedings of the 26th Annual International ACM SIGIR Conference on and Development in Information Retrieval, Special Interest Group on Information Retrieval (2003), pp. 190–197
E. Boiy, P. Hens, K. Deschacht, M.F. Moens, Automatic sentiment analysis in on-line text, in ELPUB 2007 (2007), pp 349–360
K. Shu, A. Sliva, S. Wang, J. Tang, H. Liu, Fake news detection on social media: a data mining perspective. ACM SIGKDD Exp. Newsl. 19, 22–36 (2017)
C. Makris, G. Pispirigos, I.O. Rizos, A distributed bagging ensemble methodology for community prediction in social networks. Inf. Multidisp. Digit. Publishing Inst. 11, 199 (2020)
N. Heist, S. Hertling, H. Paulheim, Language-agnostic relation extraction from abstracts in Wikis. Information 9, 75 (2018)
W. He, H. Wu, G. Yan, V. Akula, J. Shen, A novel social media competitive analytics framework with sentiment benchmarks. Inf. Manage. 52, 801–812 (2015)
M.J. Widener, W. Li, Using geolocated Twitter data to monitor the prevalence of healthy and unhealthy food references across the US. Appl. Geogr. 54, 189–197 (2014)
A. Kretinin, J. Samuel, R. Kashyap, When the going gets tough, the tweets get going! an exploratory analysis of tweets sentiments in the stock market. Am. J. Manage. 18 (2018). https://doi.org/10.33423/ajm.v18i5.251
M. De Choudhury, S. Counts, E. Horvitz, Predicting postpartum changes in emotion and behaviour via social media, in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (2013), pp. 3267–3276
Z. Wang, X. Ye, M.H. Tsou, Spatial, temporal, and content analysis of Twitter for wildfire hazards. Nat. Hazards 83, 523–540 (2016)
J. Samuel, R. Kashyap, A. Kretinin, Going where the tweets get moving! an explorative analysis of tweets sentiments in the stock market, in Proceedings of the Northeast Business & Economics Association (2018)
M.M. Skoric, J. Liu, K. Jaidka, Electoral and public opinion forecasts with social media data: a meta-analysis. Information 11, 187 (2020)
R.P. Kaila, A.V.K. Prasad, Informational flow on Twitter–corona virus outbreak–topic modelling approach. Int. J. Adv. Res. Eng. Technol. 11(3), 128–134 (2020)
S. Geman, D. Geman, Stochastic relaxation, Gibbs distributions, and the Bayesian relation of images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984)
S.K. Ghosh, S. Dey, A. Ghosh, Knowledge generation using sentiment classification involving machine learning on e-commerce: Int. J. Bus. Anal. 6(2), 74–90 (2019)
A. Ortigosa, M.J. Martin, R.M. Carro, Sentiment analysis in Facebook and its application to e-learning. Comput. Hum. Behav. 31, 527–541 (2014)
T. Pranckevicius, V. Marcinkevicius, Comparison of naive Bayes, random forest, decision tree, support vector machines, and logistic regression classifiers for text reviews classification. Balt. J. Mod. Comput. 5, 221 (2017)
W. Ramadhan, S.A. Novianty, S.C. Setianingsih, Sentiment analysis using multinomial logistic regression, in Proceedings of the 2017 International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC), Yogyakarta, Indonesia, 26–28 September 2017 (2017), pp. 46–49
T. Chen, Xu. Ruifeng, Y. He, X. Wang, Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Exp. Syst. Appl. 1, 228–239 (2017)
M.M. Mironczuk, J. Protasiewicz, W. Pedrycz, Empirical evaluation of feature projection algorithms for multi-view text classification. Exp. Syst. Appl. 1, 110–121 (2019)
A. Groß-Klußmann, S. König, M. Ebner, Buzzwords build momentum: global financial twitter sentiment and the aggregate stock market. Exp. Syst. Appl. 1, 184–197 (2019)
C.W.S. Chen, S. Lee, M.C. Dong, M. Taniguchi, What Factors Drive the Satisfaction of Citizens on Governments’ Responses to COVID-19? (JMIR Publications, 2020)
R. Chandrasekaran, V. Mehta, T. Valkunde, E. Moustakas, in Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study (JMIR Publications, 2020)
Mansoor, M., Gurumurthy, K., Anantharam, R.U., Badri Prasad, V.R.: Global sentiment analysis of COVID-19 tweets over time. Cornell Univ. J. 2 (2020)
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Tribedi, S., Biswas, A., Ghosh, S.K., Ghosh, A. (2022). Machine Learning Based Anxiety Prediction of General Public from Tweets During COVID-19. In: Nayak, J., Naik, B., Abraham, A. (eds) Understanding COVID-19: The Role of Computational Intelligence. Studies in Computational Intelligence, vol 963. Springer, Cham. https://doi.org/10.1007/978-3-030-74761-9_13
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