Twitter Sentiments and Opinions Analysis of COVID-19 Vaccine Regarding Effectiveness of Vaccine

Kritika Kumari; Sumesh Sood; Anshul Kalia; Santosh Saklani; Jyoti Negi

doi:10.17485/IJST/v17i11.2534

Article

Twitter Sentiments and Opinions Analysis of COVID-19 Vaccine Regarding Effectiveness of Vaccine

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i11.2534

Year: 2024, Volume: 17, Issue: 11, Pages: 1051-1058

Original Article

Twitter Sentiments and Opinions Analysis of COVID-19 Vaccine Regarding Effectiveness of Vaccine

Kritika Kumari^1*, Sumesh Sood², Anshul Kalia³, Santosh Saklani¹, Jyoti Negi¹

¹Research Scholar, Department of Computer Science, Himachal Pradesh University, Shimla, Himachal Pradesh, India
²Associate Professor, Department of Computer Science, Himachal Pradesh University, Shimla, Himachal Pradesh, India
³Assistant Professor, Department of Computer Science, Himachal Pradesh University, Shimla, Himachal Pradesh, India

*Corresponding Author
Email: [email protected]

Received Date:06 November 2023, Accepted Date:14 January 2024, Published Date:05 March 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: To make an extensive analysis of sentiment within the discourse surrounding COVID-19 vaccines on Twitter, employing Natural Language Processing (NLP) methodologies. Method: The research methodology encompasses data collection via the Twitter API, followed by sentiment analysis facilitated by the TextBlob library. Pre-processing stages are integrated to cleanse and standardize the Twitter data. Subsequently, sentiment analysis categorizes tweets into positive, negative, and neutral sentiments based on polarity scores. Findings: The findings, grounded in a dataset spanning from March 1, 2022, to April 30, 2022, comprising 61,934 tweets, unveil that 45.0% of tweets conveyed positive sentiment, 17.3% exhibited negativity, and 37.7% maintained neutrality. Moreover, an exploration of tweet subjectivity revealed that 70.1% of the content expressed subjectivity, while 29.9% conveyed objectivity. The research is augmented with visual representations, including word clouds and subjectivity-polarity graphs, that offer a more intuitive understanding of sentiment trends. Novelty: This study contributes to the expanding landscape of sentiment analysis and its application within the context of public health crises, empowering stakeholders with valuable knowledge to enhance vaccine acceptance and effectiveness. The tool used “Tweet Downloader” in data collection makes this study different from other reviewed studies. Keywords: COVID-19 vaccine, Twitter sentiment analysis, Public perception, Natural Language Processing (NLP), Social media data

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Copyright

© 2024 Kumari et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)