Sentiments Analysis of Covid-19 Vaccine Tweets Using Machine Learning and Vader Lexicon Method
Abstract The novel Coronavirus disease of 2019 (COVID-19) has subsequently named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) have tormented the lives of millions of people worldwide. Effective and safe vaccination might curtail the pandemic. This study aims to apply the VADER lexicon, TextBlob and machine learning approach: to analyze and detect the ongoing sentiments during the affliction of the Covid-19 pandemic on Twitter, to understand public reaction worldwide towards vaccine and concerns about the effectiveness of the vaccine. Over 200000 tweets vaccine-related using hashtags #CovidVaccine #Vaccines #CornavirusVaccine were retrieved from 18 August 2020 to 20 July 2021. Data analysis conducted by VADER lexicon method to predict sentiments polarity, counts and sentiment distribution, TextBlob to determine the subjectivity and polarity, and also compared with two other models such as Random Forest (RF) and Logistic Regression (LR). The results determine sentiments that public have a positive stance towards a vaccine follows by neutral and negative. Machine learning classification models performed better than the VADER lexicon method on vaccine Tweets. It is anticipated this study aims to help the government in long run, to make policies and a better environment for people suffering from negative thoughts during the ongoing pandemic.
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Baheti, R., & Kinariwala, S. (2019). Detection and analysis of stress using machine learning techniques. Int. J. Eng. Adv. Technol, 9, 335–342.
Bania, R. K. (2020). COVID-19 public tweets sentiment analysis using TF-IDF and inductive learning models. INFOCOMP Journal of Computer Science, 19(2), 23–41.
Bonta, V., & Janardhan, N. K. N. (2019). A comprehensive study on lexicon-based approaches for sentiment analysis. Asian Journal of Computer Science and Technology, 8(S2), 1–6.
Brunier, A., & Drysdale, C. (2020). COVID-19 disrupting mental health services in most countries, WHO survey. World Heal Organ.
Cavazos-Rehg, P. A., Krauss, M. J., Sowles, S., Connolly, S., Rosas, C., Bharadwaj, M., & Bierut, L. J. (2016). A content analysis of depression-related tweets. Computers in human behavior, 54, 351–357.
Centre for Disease control and Prevention, 2021. Variants of the virus that causes COVID-19. National Center for Immunization and Respiratory Diseases (NCIRD), Division of Viral Diseases
Chancellor, S., & De Choudhury, M. (2020). Methods in predictive techniques for mental health status on social media: a critical review. NPJ digital medicine, 3(1), 1–11.
Cherian, S., Potdar, V., Jadhav, S., Yadav, P., Gupta, N., Das, M., … & Panda, S. Convergent evolution of SARS-CoV-2 spike mutations, L452R, E484Q and P681R, in the second wave of COVID-19 in Maharashtra, India. bioRxiv 2021: 2021.04. 22.440932.
Chilwal, B., & Mishra, A. K. (2021). Extraction of Depression Symptoms From Social Networks. The Smart Cyber Ecosystem for Sustainable Development, 307–321.
Cho, G., Yim, J., Choi, Y., Ko, J., & Lee, S. H. (2019). Review of machine learning algorithms for diagnosing mental illness. Psychiatry investigation, 16(4), 262.
Das, B., & Chakraborty, S. (2018). An improved text sentiment classification model using TF-IDF and next word negation. arXiv preprint arXiv:1806.06407.
Desai, M., & Mehta, M. A. (2016, April). Techniques for sentiment analysis of Twitter data: A comprehensive survey. In 2016 International Conference on Computing, Communication and Automation (ICCCA) (pp. 149–154). IEEE.
Deshpande, M., & Rao, V. (2017, December). Depression detection using emotion artificial intelligence. In 2017 international conference on intelligent sustainable systems (iciss) (pp. 858–862). IEEE.
Dubey, A. D. (2020). Twitter sentiment analysis during COVID-19 outbreak. Available at SSRN 3572023.
Gbashi, S., Adebo, O. A., Doorsamy, W., & Njobeh, P. B. (2021). Systematic delineation of media polarity on COVID-19 vaccines in Africa: computational linguistic modeling study. JMIR medical informatics, 9(3), e22916.
Khan, H., Srivastav, A., & Mishra, A. K. (2020). Use of classification algorithms in health care. In Big Data Analytics and Intelligence: A Perspective for Health Care. Emerald Publishing Limited.
Khan, M., Rizvi, Z., Shaikh, M. Z., Kazmi, W., & Shaikh, A. (2014). Design and implementation of intelligent human stress monitoring system. International Journal of Innovation and Scientific Research, 10(1), 179–190.
Nguyen, T., Phung, D., Dao, B., Venkatesh, S., & Berk, M. (2014). Affective and content analysis of online depression communities. IEEE Transactions on Affective Computing, 5(3), 217–226.
Raichur, N., Lonakadi, N., & Mural, P. (2017). Detection of stress using image processing and machine learning techniques. International journal of engineering and technology, 9(3), 1–8.
Ritchie, H., Mathieu, E., Rodés-Guirao, L., Appel, C., Giattino, C., Ortiz-Ospina, E., … & Roser, M. (2020). Coronavirus pandemic (COVID-19). Our world in data.
Rana, M., Rehman, M. Z. U., & Jain, S. (2022, February). Comparative Study of Supervised Machine Learning Methods for Prediction of Heart Disease. In 2022 IEEE VLSI Device Circuit and System (VLSI DCS) (pp. 295–299). IEEE.
Rathi, M., Malik, A., Varshney, D., Sharma, R., & Mendiratta, S. (2018, August). Sentiment analysis of tweets using machine learning approach. In 2018 Eleventh international conference on contemporary computing (IC3) (pp. 1–3). IEEE.
Saladino, V., Algeri, D., & Auriemma, V. (2020). The psychological and social impact of Covid-19: new perspectives of well-being. Frontiers in psychology, 2550.
Shatte, A. B., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: a scoping review of methods and applications. Psychological medicine, 49(9), 1426–1448.
Subhani, A. R., Mumtaz, W., Saad, M. N. B. M., Kamel, N., & Malik, A. S. (2017). Machine learning framework for the detection of mental stress at multiple levels. IEEE Access, 5, 13545–13556.
Terry, M. (2021). UPDATED comparing COVID-19 vaccines: timelines, types, and prices. BioSpace. April 23.
Troussas, C., Virvou, M., Espinosa, K. J., Llaguno, K., & Caro, J. (2013, July). Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning. In IISA 2013 (pp. 1–6). IEEE.
World Economic Forum, 2020. https://www.weforum.org/agenda/2020/08/covid-19-coronavirus-mental-health-well-being-countries/
Yazdavar, A. H., Mahdavinejad, M. S., Bajaj, G., Thirunarayan, K., Pathak, J., & Sheth, A. (2018, June). Mental health analysis via social media data. In 2018 IEEE International Conference on Healthcare Informatics (ICHI) (pp. 459–460). IEEE.
Arya, V., Mishra, A. K., & González-Briones, A. (2023). Sentiments Analysis of Covid-19 Vaccine Tweets Using Machine Learning and Vader Lexicon Method. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(4), 507–518. https://doi.org/10.14201/adcaij.27349
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