Chinnasamy, P., Suresh, V., Ramprathap, K., Jency, B., Jebamani, A., Srinivas Rao, K. and Shiva Kranthi, M. (2022) COVID-19 Vaccine Sentiment Analysis Using Public Opinions on Twitter. Materials Today: Proceedings, 64, 448-451.
https://doi.org/10.1016/j.matpr.2022.04.809

has been cited by the following article:

• TITLE: Analysis of Public Sentiment regarding COVID-19 Vaccines on the Social Media Platform Reddit

  AUTHORS: Lucien Dikla Ngueleo, Jules Pagna Disso, Armel Ayimdji Tekemetieu, Justin Moskolaï Ngossaha, Michael Nana Kameni

  KEYWORDS: COVID-19 Vaccine, TextBlob, Twitter-RoBERTa-Base-Sentiment, Sentiment Analysis, Latent Dirichlet Allocation

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.2, February 21, 2024

  ABSTRACT: This study undertakes a thorough analysis of the sentiment within the r/Corona-virus subreddit community regarding COVID-19 vaccines on Reddit. We meticulously collected and processed 34,768 comments, spanning from November 20, 2020, to January 17, 2021, using sentiment calculation methods such as TextBlob and Twitter-RoBERTa-Base-sentiment to categorize comments into positive, negative, or neutral sentiments. The methodology involved the use of Count Vectorizer as a vectorization technique and the implementation of advanced ensemble algorithms like XGBoost and Random Forest, achieving an accuracy of approximately 80%. Furthermore, through the Dirichlet latent allocation, we identified 23 distinct reasons for vaccine distrust among negative comments. These findings are crucial for understanding the community’s attitudes towards vaccination and can guide targeted public health messaging. Our study not only provides insights into public opinion during a critical health crisis, but also demonstrates the effectiveness of combining natural language processing tools and ensemble algorithms in sentiment analysis.