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      COVID - 19 Vaccine Sentiment Analysis using Public Opinions on Twitter

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          Abstract

          Twitter, as is well known, is one of the most active social media platforms, with millions of tweets posted every day, in which different people express their opinions on topics such as travel, economic concerns, political decisions, and so on. As a result, it is a useful source of knowledge. We offer Sentiment Analysis using Twitter Data for the research. Initially, our technology retrieves currently accessible tweets and hashtags about various types of covid vaccinations posted on Twitter through using Twitter's API. Following that, the imported Tweets are automatically configured to generate a collection of untrained rules and random variables. To create our model, we're utilizing, Tweepy, which is a wrapper for Twitter's API. Following that, as part of the sentiment analysis of new Messages, the software produces donut graphs.

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          Mapping global trends in vaccine confidence and investigating barriers to vaccine uptake: a large-scale retrospective temporal modelling study

          Summary Background There is growing evidence of vaccine delays or refusals due to a lack of trust in the importance, safety, or effectiveness of vaccines, alongside persisting access issues. Although immunisation coverage is reported administratively across the world, no similarly robust monitoring system exists for vaccine confidence. In this study, vaccine confidence was mapped across 149 countries between 2015 and 2019. Methods In this large-scale retrospective data-driven analysis, we examined global trends in vaccine confidence using data from 290 surveys done between September, 2015, and December, 2019, across 149 countries, and including 284 381 individuals. We used a Bayesian multinomial logit Gaussian process model to produce estimates of public perceptions towards the safety, importance, and effectiveness of vaccines. Associations between vaccine uptake and a large range of putative drivers of uptake, including vaccine confidence, socioeconomic status, and sources of trust, were determined using univariate Bayesian logistic regressions. Gibbs sampling was used for Bayesian model inference, with 95% Bayesian highest posterior density intervals used to capture uncertainty. Findings Between November, 2015, and December, 2019, we estimate that confidence in the importance, safety, and effectiveness of vaccines fell in Afghanistan, Indonesia, Pakistan, the Philippines, and South Korea. We found significant increases in respondents strongly disagreeing that vaccines are safe between 2015 and 2019 in six countries: Afghanistan, Azerbaijan, Indonesia, Nigeria, Pakistan, and Serbia. We find signs that confidence has improved between 2018 and 2019 in some EU member states, including Finland, France, Ireland, and Italy, with recent losses detected in Poland. Confidence in the importance of vaccines (rather than in their safety or effectiveness) had the strongest univariate association with vaccine uptake compared with other determinants considered. When a link was found between individuals' religious beliefs and uptake, findings indicated that minority religious groups tended to have lower probabilities of uptake. Interpretation To our knowledge, this is the largest study of global vaccine confidence to date, allowing for cross-country comparisons and changes over time. Our findings highlight the importance of regular monitoring to detect emerging trends to prompt interventions to build and sustain vaccine confidence. Funding European Commission, Wellcome, and Engineering and Physical Sciences Research Council.
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            An Analysis of COVID-19 Vaccine Sentiments and Opinions on Twitter

            Objective We identified public sentiments and opinions toward the COVID-19 vaccines based on the content of Twitter. Materials and Methods We retrieved 4,552,652 publicly available tweets posted within the timeline of January 2020 to January 2021. Following extraction, we identified vaccine sentiments and opinions of tweets and compared their progression by time, geographical distribution, main themes, keywords, posts engagement metrics and accounts characteristics. Results We found a slight difference in the prevalence of positive and negative sentiments, with positive being the dominant polarity and having higher engagements. The amount of discussion on vaccine rejection and hesitancy was more than interest in vaccines during the course of the study, but the pattern was different in various countries. We found the accounts producing vaccine opposition content were partly Twitter bots or political activists while well-known individuals and organizations generated the content in favour of vaccination. Conclusion Understanding sentiments and opinions toward vaccination using Twitter may help public health agencies to increase positive messaging and eliminate opposing messages in order to enhance vaccine uptake.
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              Using Twitter for sentiment analysis towards AstraZeneca/Oxford, Pfizer/BioNTech and Moderna COVID-19 vaccines

              Introduction A worldwide vaccination campaign is underway to bring an end to the SARS-CoV-2 pandemic; however, its success relies heavily on the actual willingness of individuals to get vaccinated. Social media platforms such as Twitter may prove to be a valuable source of information on the attitudes and sentiment towards SARS-CoV-2 vaccination that can be tracked almost instantaneously. Materials and methods The Twitter academic Application Programming Interface was used to retrieve all English-language tweets mentioning AstraZeneca/Oxford, Pfizer/BioNTech and Moderna vaccines in 4 months from 1 December 2020 to 31 March 2021. Sentiment analysis was performed using the AFINN lexicon to calculate the daily average sentiment of tweets which was evaluated longitudinally and comparatively for each vaccine throughout the 4 months. Results A total of 701 891 tweets have been retrieved and included in the daily sentiment analysis. The sentiment regarding Pfizer and Moderna vaccines appeared positive and stable throughout the 4 months, with no significant differences in sentiment between the months. In contrast, the sentiment regarding the AstraZeneca/Oxford vaccine seems to be decreasing over time, with a significant decrease when comparing December with March (p<0.0000000001, mean difference=−0.746, 95% CI=−0.915 to −0.577). Conclusion Lexicon-based Twitter sentiment analysis is a valuable and easily implemented tool to track the sentiment regarding SARS-CoV-2 vaccines. It is worrisome that the sentiment regarding the AstraZeneca/Oxford vaccine appears to be turning negative over time, as this may boost hesitancy rates towards this specific SARS-CoV-2 vaccine.
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                Author and article information

                Journal
                Mater Today Proc
                Mater Today Proc
                Materials Today. Proceedings
                Elsevier Ltd.
                2214-7853
                28 April 2022
                28 April 2022
                Affiliations
                [a ]Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, India
                [b ]Department of Computer Science and Engineering, Dr.N.G.P. Institute of Technology, Coimbatore, India
                [c ]Department of Management Studies, M.Kumarasamy College of Engineering, Karur, India
                [d ]Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, India
                [e ]Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, India
                [f ]UG Student, Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, India
                Author notes
                [* ]Corresponding author.
                Article
                S2214-7853(22)02986-8
                10.1016/j.matpr.2022.04.809
                9046075
                35502322
                61e17dc1-d8e6-4f7d-b5d9-f572c9b311fe
                Copyright © 2022 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Advanced Materials for Innovation and Sustainability.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 11 April 2022
                : 19 April 2022
                : 21 April 2022
                Categories
                Article

                public sentiments,tweets,hashtags on covid vaccines,sentiment analysis,machine learning algorithms

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