## 6 Conclusions  
This is a study of COVID-19 vaccination in the aftermath of the 2020 COVID-19 outbreak with quantitative analysis. We analyze public sentiment on different COVID-19 vaccines from Twitter dataset. We find that the public sentiment is more positive than negative despite some side effects found in some of the vaccines. Although the majority of the tweets (60-70%) are deemed neutral, the remaining part consists of mostly positive tweets (20-25%). We also do an analysis of the public's healthy lifestyle after vaccination. We find that people are conscious about maintaining social distancing with positive sentiments of 70% where neutral sentiment was much less (5%) compared to higher neutral sentiments in the remaining cases. People are also positive about maintaining hygiene with 40% and 10% positive and negative sentiments respectively. Although we observe that people are more reluctant to using masks and more prone to travel or do social gathering after vaccination, there is less difference between the positive and negative sentiments. However, it is a relief that the negative sentiments do not exceed the positive sentiments in any of the cases. We also show a time series forecast of the US population to be vaccinated for a time period of 2 months. Our vaccination forecast model predicts  
Figure 15: COVID-19 vaccination prediction to forecast partially vaccinated population in the USA for different machine learning models with forecast time of 2 months. that around 62.44% of the total population will get at least one dose vaccine and 48% will be fully vaccinated by the end of July 2021. Our prediction model gives a similar estimate of having partial vaccination of the adults to be 73.53% that the US Government is projecting to be 70% on Independence Day (4 July 2021). The results of these analyses can be utilized to better comprehend Twitter users' opinions about COVID-19 vaccination and their lifestyle after vaccination. The current discoveries give a steppingstone to measure the public's conversation about COVID-19 vaccination and guidelines for a healthy way of life during the pandemic. This will help the public health authorities and policymakers to understand how individuals are endeavoring to adapt with their mental conditions during these extraordinary times and what services and resources should be made accessible to the public. This study will help health and government officials to better comprehend and plan vaccination campaigns. The constraints of this exploration are in the dataset size and time period during which it was collected. It would be interesting to have information from a longer time span to perceive how sentiments change over a longer time. especially when the pandemic comes to an end. Classifying tweets into different emotions, such as inspired, happy, annoyed, sad, angry, afraid, etc., to fully understand and reveal the sentiment of the tweets is also an interesting future direction. Conceptualization, N.S.S. and S.A.; methodology, N.S.S. and S.A.; software, N.S.S. ; validation, N.S.S. ; formal analysis, N.S.S. ; investigation, N.S.S. ; resources, N.S.S. ; data curation, N.S.S. ; writing--original draft preparation, N.S.S. ; writing--review and editing, N.S.S. and S.A.; visualization, N.S.S. ; supervision, S.A.; project administration, S.A.; funding acquisition, S.A. Both authors have read and agreed to the published version of the manuscript. This work has been partially supported by Louisiana Board of Regents RCS Grant LEQSF (2017-20)-RDA-25. A part of data is made available according to Twitter policy in the publicly available code-base. Full data needs to be extracted from Twitter. We thank the anonymous reviewers for the helpful comments and suggestions to improve this paper. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: