Exploring YouTube Comments to Understand Public Sentiment on COVID-19 Vaccines through Deep Learning-based Sentiment Analysis

  • Mohd Suffian Sulaiman 1School of Computing Sciences, College of Computing, Informatics & Mathematics, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Farizul Azlan Maskan 1School of Computing Sciences, College of Computing, Informatics & Mathematics, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Zuraidah Derasit School of Mathematical Sciences, College of Computing, Informatics & Mathematics, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Noor Hasimah Ibrahim Teo School of Computing Sciences, College of Computing, Informatics & Mathematics, Universiti Teknologi MARA, Melaka Branch, Jasin Campus, 77300 Merlimau, Melaka, Malaysia

Abstract

COVID-19 was first found in China in 2019. Since then, it has quickly spread around the world, which has led to a lot of news stories and social media posts about the pandemic. YouTube, a popular video-sharing website, has become a valuable source of information on COVID-19 and other topics. However, it can be difficult to extract useful insights from the vast array of user comments that accompany these videos. One potential method for understanding public sentiment is to use sentiment analysis, which involves classifying text as positive, negative, or neutral. In this study, the dataset of over 44,000 YouTube comments related to COVID-19 vaccines was used, which was filtered to a total of 16,073 comments for analysis. The data was cleaned and organised using NeatText and then processed using GloVe word embedding, a technique for establishing statistical relationships between words. Based on the experiment, the performances of three different types of deep learning techniques: recurrent neural networks (RNN), gated recurrent units (GRU) and long short-term memory (LSTM) are compared in accurately classifying the sentiment of the comments. The study found that the GRU had the highest accuracy of 80.19%, followed by the LSTM with 79.00% accuracy, and the RNN with 67.15% accuracy.

Published
2023-10-31
How to Cite
Sulaiman, M. S., Maskan, F. A., Derasit, Z., & Ibrahim Teo, N. H. (2023). Exploring YouTube Comments to Understand Public Sentiment on COVID-19 Vaccines through Deep Learning-based Sentiment Analysis . Malaysian Journal of Applied Sciences, 8(2), 13-27. https://doi.org/10.37231/myjas.2023.8.2.353
Section
Research Articles