The Covid-19 outbreak has created a challenge for the whole of mankind and impacted everyday life worldwide. The pandemic had a devastating effect on many people. It also created anxiety, fear, and depression among people. To eradicate the disease, many scientists at major pharmaceutical companies and institutes working together to develop vaccine. Social media is one of the best platforms to discuss the latest trending topics and express your view about them. The Covid-19 Vaccine promotion across the world has created lots of discussion in Twitter, social media platforms where user love to express their feeling and opinion. However, due to lack of knowledge and understanding about Covid – 19 vaccines, it has created negative sentiments towards vaccine among few. Also, there has not been much research work done on in-depth analysis of people’s opinion or sentiment towards various vaccine and its brands. In this study will be using publicly available Covid-19 vaccine tweets to understand public opinion or feeling about various covid-19 vaccine brands. This research will be using publicly available Covid-19 vaccine tweets to understand public opinion or feeling about various Covid-19 vaccine brands. The study was conducted using publicly available datasets from online resource, Kaggle. The dataset contains 228207 tweets from Kaggle about the opinion for Covid-19 vaccines during 12 December 2020 to 24 November 2021. After the extraction, vaccine sentiments identified across all brands. After identification of sentiments, accuracy be evaluated based on prediction using various metrics. This research will find the difference in public opinion on Covid-19 vaccines. Understanding the sentiments and public opinions towards vaccine will help health agencies to increase positive awareness about covid-19 vaccine across world.

Covid-19 Vaccine, Corona Vaccine, Sentiment Analysis, Twitter Sentiments, Machine Learning, Deep Learning

[1] Fuyong Zhang, Wang Yi, Liu Shigang, Wang Hua. Decision-based evasion attacks on tree ensemble classifiers. World Wide Web; 23(5):2957–77, 2020.
[2] Vohra, S., & Teraiya, J. (2013). A Comparative Study of Sentiment Analysis Techniques. International Journal of Information, Knowledge and Research in Computer Engineering, 2(2), pp.313-317, 2013.
[3] Machine Learning & its Applications Outsource to India, 2020.
[4] Jain, A. P., & Dandannavar, P. (2016). Application of machine learning techniques to sentiment analysis. Second International Conference on Applied and Theoretical Computing and Communication Technology (ICATccT), 1(1). pp.628–632, 2016. DOI: https://doi.org/10.1109/ICATCCT.2016.7912076
[5] Chakraborty K, Bhatia S, Bhattacharyya S, Platos J, Bag R, Hassanien AE. Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media. Appl Soft Comp. 2020;97:106754. https://doi.org/10.1016/j.asoc.2020.106 754 PMid:33013254 PMCid:PMC7521435
[6] Praveen SV, Ittamalla R. General public’s attitude toward governments implementing digital contact tracing to curb COVID-19–a study based on natural language processing. Int J Per Comp Comm. 2020. https://doi.org/ 10.1108/IJPCC-09-2020-0121
[7] Chen Y, Yuan J, You Q, Luo J. Twitter sentiment analysis via bi-sense emoji embedding and attention-based LSTM. In Proceedings of the 26th ACM international conference on Multimedia, pp.117-125, 2018. https://doi.org/ 10.1145/3240508.3240533
[8] Reddy DM, Reddy DN. Twitter Sentiment Analysis using Distributed Word and Sentence Representation. arXiv preprint arXiv:1904.12580. 2019
[9] Hasan A, Moin S, Karim A, Shamshirband S. Machine learning-based sentiment analysis for twitter accounts. Math Comp App. 2018;23(1):11. https://doi.org/10.3390/ mca23010011
[10] Xue J, Chen J, Chen C, Zheng C, Li S, Zhu T. Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter. PloS one. 2020;15(9):e0239441. https://doi.org/10.1371/journal.pone .0239441 PMid:32976519 PMCid:PMC7518625
[11] Sanders AC, White RC, Severson LS, Ma R, McQueen R, Paulo HCA, Bennett KP. Unmasking the conversation on masks: Natural language processing for topical sentiment analysis of COVID-19 Twitter discourse. medRxiv, 2020-08. 2021. https://doi.org/10.1101/2020.08.28.20183863
[12] Gupta P, Kumar S, Suman RR, Kumar V. Sentiment Analysis of Lockdown in India During COVID-19: A Case Study on Twitter. IEEE Trans Comp Soc Sys. 2020. https://doi.org/10.1109/TCSS.2020.3042446
[13] Das S, Kolya AK. Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network. Evol Intell. 2021:1-22. https://doi.org/10.1007/s12065-021-00598-7 PMid:33815622 PMCid:PMC8007226
[14] Gambino OJ, Calvo H, García-Mendoza CV. Distribution of emotional reactions to news articles in twitter. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). 2018.
[15] Shahriar, K.T., Islam, M.N., Anwar, M.M. and Sarker, I.H., (2022) COVID-19 analytics: Towards the effect of vaccine brands through analyzing public sentiment of tweets. Informatics in Medicine Unlocked, [online] 31February, p.100969, 2022. Available at: https://doi.org/10.1016/j.imu.2022.100969