Social media sentiment analysis on third booster dosage for COVID-19 vaccination: a holistic machine learning approach

Ghosh, Papri, Dutta, Ritam, Agarwal, Nikita, Chatterjee, Siddhartha and Mitra, Solanki 2023. Social media sentiment analysis on third booster dosage for COVID-19 vaccination: a holistic machine learning approach. Presented at: International Conference on Intelligent Systems and Human-Machine Collaboration 2022, Published in: Bhattacharyya, Siddhartha, Koeppen, Mario, De, Debashis and Piuri, Vincenzo eds. Intelligent Systems and Human Machine Collaboration. , vol.985 Springer Science Business Media, 10.1007/978-981-19-8477-8_14

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Abstract

Over a period of more than two years the public health has been experiencing legitimate threat due to COVID-19 virus infection. This article represents a holistic machine learning approach to get an insight of social media sentiment analysis on third booster dosage for COVID-19 vaccination across the globe. Here in this work, researchers have considered Twitter responses of people to perform the sentiment analysis. Large number of tweets on social media require multiple terabyte sized database. The machine learned algorithm-based sentiment analysis can actually be performed by retrieving millions of twitter responses from users on daily basis. Comments regarding any news or any trending product launch may be ascertained well in twitter information. Our aim is to analyze the user tweet responses on third booster dosage for COVID-19 vaccination. In this sentiment analysis, the user sentiment responses are firstly categorized into positive sentiment, negative sentiment, and neutral sentiment. A performance study is performed to quickly locate the application and based on their sentiment score the application can distinguish the positive sentiment, negative sentiment and neutral sentiment-based tweet responses once clustered with various dictionaries and establish a powerful support on the prediction. This paper surveys the polarity activity exploitation using various machine learning algorithms viz. Naïve Bayes (NB), K- Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), and Valence Aware wordbook and sEntiment thinker (VADER) on the third booster dosage for COVID-19 vaccination. The VADER sentiment analysis predicts 97% accuracy, 92% precision, and 95% recall compared to other existing machine learning models.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	Springer Science Business Media
ISBN:	978-981-19-8476-1
Date of First Compliant Deposit:	17 January 2024
Date of Acceptance:	30 March 2023
Last Modified:	16 Feb 2024 16:15
URI:	https://orca.cardiff.ac.uk/id/eprint/165612

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