COVID-19 Vaccine Sensing: Sentiment Analysis from Twitter Data

Han, Xu; Liu, Ruixin; Luo, Ziling; Xu, Minghua; Wang, Bang

doi:10.1109/smc52423.2021.9658764

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…On the other hand, they have hardly ever concentrated on the vaccine. In [11] , we investigate the topic of the COVID-19 vaccine that was discussed on Twitter. To be more specific, all of the tweets pertaining to the COVID-19 vaccine that were posted on Twitter between December 15th, 2020 and February 10th, 2021 were gathered with the help of the Twitter API.…”

Section: Related Workmentioning

confidence: 99%

Topic modelling and sentiment analysis architecture for social networks in the COVID pandemic

Upadhyay

2022

Int. J. Comput. Artif. Intell.

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The goal of this research is to identify the topics and feelings in the community COVID-19 vaccinerelated debate on social networks and to decipher the pertinent adjustments in subjects and feelings atop gray moment in order to study the public's perception, concerns, and feelings that may meet a variety of vaccination rates goals. This will be carried out in order to track down the themes and opinions expressed in social media conversations around the COVID-19 vaccination. The World Health Organization declared COVID-19 a pandemic on March 11, 2020, and from that day until January 31, 2021, messages were retrieved from a sizable COVID-19 Twitter buzz data collection. We sorted through the tweets and only preserved the ones that contained the phrases immunisation, vaccination, vaccine, injections, and inoculated. And used the Dictionary, we carried out latent Dirichlet allocations for language models as well as emotion and emotional assessment. Both of these studies were completed with R's assistance. Outcomes Theme modelling was used to generate a total of 16 topics from twitter about the COVID-19 vaccine, which were then classified into 5 broad categories. People's views on vaccinations these were biggest trending topic on Twitter (227,840 out of 1,499,421 tweets, or 15.2%), and it stayed so for the most of the period we were examining the information. Additionally, the conversation included presentations with a broader perspective. Due to the increasing optimism around these vaccinations and the dominantly sense of trust displayed in the debate on social networks, it's probable that COVID-19 vaccinations are more widely accepted than previous vaccinations.

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Section: Related Workmentioning

confidence: 99%

Topic modelling and sentiment analysis architecture for social networks in the COVID pandemic

Upadhyay

2022

Int. J. Comput. Artif. Intell.

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A statistical analysis of tweets on covid-19 vaccine hesitancy utilizing opinion mining: an Indian perspective

Verma

Chhabra

Gupta

2022

Soc. Netw. Anal. Min.

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The world witnessed the emergence of a deadly virus in December 2019, later named COVID-19. The virus was found to be highly contagious, and so people across the world were highly prone to be affected by the virus. Being a virus-borne disease, developing a vaccine was one of the most promising remedies. Thus, research organizations across the globe started working on developing the vaccine. However, it was later found by many researchers that a large number of people were hesitant to receive the vaccine. This paper aims to study the acceptance and hesitancy levels of people in India and compares them with the acceptance and hesitancy levels of people from the UK, the USA, and the rest of the world by analyzing their tweets on Twitter. For this study, 2,98,452 tweets were fetched from January 2020 to March 2022 from Twitter, and 1,84,720 tweets from 1,22,960 unique users were selected based on their country of origin. Machine learning based Sentiment analysis is then used to evaluate and analyze the tweets. The paper also proposes an NLP-based algorithm to perform opinion mining on Twitter data. The study found the public sentiment of the Indian population to be 63% positive, 28% neutral, and 9% negative. While the worldwide sentiment distribution is 45% positive, 34% neutral, and 21% negative, the USA has 42% positive, 34% neutral, and 23% negative and the UK has 50% positive, 29% neutral, and 21% negative. Also, sentiment analysis for individual vaccines in Indian context resulted in “Covaxin” with the highest positive sentiment at 43% followed by “Covishield” at 36%. The outcome of this work yields an insight into the public perception of the COVID-19 vaccine and thus can be used to formulate policies for existing and future vaccine campaigns. This study becomes more relevant as it is the consolidated opinion of Indian people, which is versatile in nature.

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Sentiment Analysis of Covid Vaccine Myths using Various Data Visualization Tools

Bhatia,

Rathi,

Singh

et al. 2024

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Anti-vaccination agitation is on the rise, both in-person and online, notably on social media. The Internet has become the principal source of health-related information and vaccines for an increasing number of individuals. This is worrisome since, on social media, any comment, whether from a medical practitioner or a layperson, has the same weight. As a result, low-quality data may have a growing influence on vaccination decisions for children. OBJECTIVES: This paper will evaluate the scale and type of vaccine-related disinformation, the main purpose was to discover what caused vaccine fear and anti-vaccination attitudes among social media users. METHODS: The vaccination-related data used in this paper was gathered from Reddit, an information-sharing social media network with about 430 million members, to examine popular attitudes toward the vaccine. The materials were then pre-processed. External links, punctuation, and bracketed information were the first things to go. All text was also converted to lowercase. This was followed by a check for missing data. This paper is novel and different as Matplotlib, pandas, and word cloud was used to create word clouds and every result has a visual representation. The Sentiment analysis was conducted using the NLTK library as well as polarity and subjectivity graphs were generated. RESULTS: It was discovered that the majority population had neutral sentiments regarding vaccination. Data visualization methods such as bar charts showed that neutral sentiment outnumbers both positive and negative sentiment. CONCLUSION: Prevalent Sentiment has a big influence on how people react to the media and what they say, especially as people utilize social media platforms more and more. Slight disinformation and/or indoctrination can quickly turn a neutral opinion into a negative one.

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COVID-19 Vaccine Sensing: Sentiment Analysis from Twitter Data

Cited by 3 publications

References 23 publications

Topic modelling and sentiment analysis architecture for social networks in the COVID pandemic

Topic modelling and sentiment analysis architecture for social networks in the COVID pandemic

A statistical analysis of tweets on covid-19 vaccine hesitancy utilizing opinion mining: an Indian perspective

Sentiment Analysis of Covid Vaccine Myths using Various Data Visualization Tools

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