Sentiment Analysis on Covid-19 Vaccination Using Machine Learning Techniques

DOI

10.2991/978-94-6463-136-4_22How to use a DOI?

Keywords

Machine Learning; Sentiment Analysis; Lexicons; COVID-19; Covaxin; Covishield; Sputnik

Abstract

In this research work, we have performed Machine Learning and Lexicon Based Techniques to identify and analyze user’s expression or opinion on covid-19 vaccination from social media platform that is Twitter and acquainted the bulk tweets from 01 June 2021 to August 2021 using various twitter hashtags. Machine learning based Classifiers are used for investigating the evaluation performance of Algorithms. Real time datasets and machine Learning Algorithms are compared with Best Data classification Evaluation based on the size of train data also another approach is to investigating the polarity by using Lexicon Based approach for this Bing Liu Lexicons and Stanford University Lexicons are used. The global pandemic has created the medical emergency and stops the many regular activities. The whole world in the lockdown or quarantine to because Coronavirus disease. Among them, Covaxin, Covishield, Pfizer, Moderna and SputnikVare popular. Universally publicare articulating opinions on protection and success of the vaccines on social media. Research article shows, such tweets are collected from developer Application Management using a Twitter API. Unprocessed tweets are kept and preprocessed through Machine Learning techniques. Users opinion are predicted using a Classifiers Decision Tree, Support Vector Machine, K NN Algorithm and Naïve Bayes. Comparative machine learning classifiers study here comparative analysis is got highest accuracy of 97% for Decision tree with Covaxin dataset, Support vector machine with 94% for SputnikV, Naïve Bayes got highest accuracy of 95 for Covishield dataset and KNN got Highest accuracy of 96% for Covaxin. The Lexicon Based polarity classifies the score into three users opinions, positive, negative, and neutral. Result shows that, Covaxin shows 28.14% positive, 12.5% negative, and 59.36% neutral sentiment. Covishield shows 17.62% positive, 15.04% negative, and 67.34% neutral sentiment. Moderna shows 23.68% positive, 19.28% negative, and 57.02% neutral Pfizer shows 18.28% positive, 34.06% negative, and 47.66% neutral, SputnikV shows 24.62% positive, 14.1% negative, and 61.28% neutral.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Ashish A. Bhalerao
AU  - Bharat R. Naiknaware
AU  - Ramesh R. Manza
AU  - Shobha K. Bawiskar
PY  - 2023
DA  - 2023/05/01
TI  - Sentiment Analysis on Covid-19 Vaccination Using Machine Learning Techniques
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 235
EP  - 250
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_22
DO  - 10.2991/978-94-6463-136-4_22
ID  - Bhalerao2023
ER  -