#Vaccine: Using Hashtags from Indian Tweets to Capture and Analyse the Sentiments of People on Vaccination for Covid’19 Pandemic

Anuratha, K.; Sujeetha, S.; Nandhini, J. M.; Priya, Balakrishnan Thenmozhi; Paravthy, M.

doi:10.3233/apc210183

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Twitter data can be collected and analysed through open academic access via API v2, making it a valuable tool for researchers, government organizations, and health care professionals (Luo et al, 2021). A number of studies have analysed tweets using techniques such as logistic regression, deep learning algorithms, topic modelling, Latent Dirichlet Allocation (LDA), and machine learning to better understand the conversations, concerns, sentiments, and reactions elicited by tweets (Li Han Wong et al,2022;Amjad et al 2021;Deverna et al 2021;Anuratha et al, 2021;Alfatease et al, 2021;Piltch-Loeb et al, 2021).…”

Section: Use Of Twitter To Determine Public Sentiments On Covid-19 Va...mentioning

confidence: 99%

“…By analysing Twitter data, researchers and health care professionals can gain a deeper understanding of public sentiment and reactions, and use this information to inform vaccine policy and communication efforts. Vaccine manufacturers can also use sentiment analysis to quickly detect negative feelings towards their vaccine and respond accordingly (Anuratha et al, 2021;Hou et al, 2021).…”

Section: Use Of Twitter To Determine Public Sentiments On Covid-19 Va...mentioning

confidence: 99%

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Sentiment Analysis of South African tweets about COVID-19 vaccines

Ditsele¹,

Chigona²,

Sephodi³

EPiC Series in Education Science

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The COVID-19 pandemic and lockdowns have led to a surge in the use of social media for information sharing and learning about the virus and the vaccine. Our study aimed to understand the sentiments of South African Twitter users towards COVID-19 vaccines. Using the Twitter API version 2, we collected a total of 21,084 tweets from 1 January 2021 to 31 December 2021, during the government's rollout of the vaccine. A sentiment analysis was performed using the VADER lexicon-based classifier to categorise the tweets into positive, neutral, and negative sentiments. The results showed that 40% of the tweets were positive towards the vaccine, 32% were neutral, and 28% were negative. The analysis also revealed that people expressed their opinions on vaccinations more frequently during the early months of the year (January-March 2021) in response to the government's announcement for the vaccine rollout. However, the attitudes towards the vaccine changed throughout the year, indicating that people were sceptical of the government's vaccine rollout strategy, which could have affected the overall vaccine adoption. The findings of this study can provide valuable insights for policymakers and healthcare organisations in shaping effective strategies for promoting vaccine adoption.

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Section: Use Of Twitter To Determine Public Sentiments On Covid-19 Va...mentioning

confidence: 99%

Section: Use Of Twitter To Determine Public Sentiments On Covid-19 Va...mentioning

confidence: 99%

Sentiment Analysis of South African tweets about COVID-19 vaccines

Ditsele¹,

Chigona²,

Sephodi³

EPiC Series in Education Science

View full text Add to dashboard Cite

show abstract

It’s Your Turn, Are You Ready to Get Vaccinated? Towards an Exploration of Vaccine Hesitancy Using Sentiment Analysis of Instagram Posts

et al. 2022

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The deadly threat caused by the rapid spread of COVID-19 has been restricted by virtue of vaccines. However, there is misinformation regarding the certainty and positives outcome of getting vaccinated; hence, many people are reluctant to opt for it. Therefore, in this paper, we identified public sentiments and hesitancy toward the COVID-19 vaccines based on Instagram posts as part of intelligent surveillance. We first retrieved more than 10k publicly available comments and captions posted under different vaccine hashtags (namely, covaxin, covishield, and sputnik). Next, we translated the extracted comments into a common language (English), followed by the calculation of the polarity score of each comment, which helped identify the vaccine sentiments and opinions in the comments (positive, negative, and neutral) with an accuracy of more than 80%. Moreover, upon analysing the sentiments, we found that covaxin received 71.4% positive, 18.5% neutral, and 10.1% negative comments; covishield obtained 64.2% positive, 24.5% neutral, and 11.3% negative post; and sputnik received 55.8% positive, 15.5% neutral, and 28.7% negative sentiments. Understanding vaccination perceptions and views through Instagram comments, captions, and posts is helpful for public health officials seeking to enhance vaccine uptake by promoting positive marketing and reducing negative marketing. In addition to this, some interesting future directions are also suggested considering the investigated problem.

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#Vaccine: Using Hashtags from Indian Tweets to Capture and Analyse the Sentiments of People on Vaccination for Covid’19 Pandemic

Cited by 2 publications

References 6 publications

Sentiment Analysis of South African tweets about COVID-19 vaccines

Sentiment Analysis of South African tweets about COVID-19 vaccines

It’s Your Turn, Are You Ready to Get Vaccinated? Towards an Exploration of Vaccine Hesitancy Using Sentiment Analysis of Instagram Posts

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