Clustering based sentiment analysis on Twitter data for COVID-19 vaccines in India

K Ponmani; M Thangaraj

doi:10.53730/ijhs.v6nS2.6126

Authors

Ponmani K
ponmanidurai@gmail.com
Bharathiar University, Coimbatore, India
Thangaraj M Madurai Kamaraj University, Madurai, India

Keywords:

COVID-19 vaccines, improved random forest, machine learning, MEEM, sentiment analysis

Abstract

Coronavirus is a new and rapidly spreading viral disease. It is essential to have a vaccine in order to reduce the virus's impact. Vaccination-related sentiments can influence an individual's decision to accept the vaccines. Evaluating the sentiments is a time-consuming and challenging process. Sentiment analysis (SA) could have an impact on the vaccination initiatives as well as changes in people's opinions and behaviour around immunizations. Since social media is widely utilized to disseminate information, mining this data is a popular area of study these days. On Twitter, a wide range of opinions about the negative effects of licensed vaccines have been expressed over time. In this research, tweets are gathered, pre-processed to remove extraneous data, and then utilized for sentiments analysis utilizing the Lexicons-based technique and machine learning. After feature extraction, the clustering is performed using MEEM approach. This research proposed a Clustering Based Twitter sentiments analysis of COVID 19 (C-SAT COVID 19) vaccinations in India. An enhanced random forest classifier is implemented in this research to classify the sentiment scores provided by the sentiment analysis. A classification is performed based on the negative, neutral, and positive sentiment analysis to examine people's emotions towards vaccinations accessible in India.

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