COVID-19 Tweets Sentiment Classification: A Deep Learning Model with Stacking Ensemble for Improved Performance

Solomon Eshun¹ , Alberta Araba Johnson² , Richmond Essieku³

1. School of Mathematical and Statistical Sciences, The University of Texas Rio Grande Valley, Edinburg, TX, United States.
2. School of Mathematical and Statistical Sciences, The University of Texas Rio Grande Valley, Edinburg, TX, United States.
3. Department of Economics, Texas Tech University, Lubbock, TX, United States.

Section:Research Paper, Product Type: Journal-Paper
Vol.10 , Issue.3 , pp.1-8, Jun-2023

Online published on Jun 30, 2023

Copyright © Solomon Eshun, Alberta Araba Johnson, Richmond Essieku . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The global impact of COVID-19 has resulted in profound consequences on society, the economy, and public health. In response to this, individuals have turned to social media platforms such as Twitter as a means to voice their sentiments and viewpoints regarding the pandemic. Sentiment analysis can be used to identify public concerns and challenges, such as misinformation and negative attitudes towards vaccinations and public health measures. However, manually analyzing such vast amounts of data is laborious and expensive. Deep learning models have demonstrated encouraging outcomes for sentiment analysis, due to their ability to learn complex patterns and relationships within datasets. As a result, this study aims to use deep learning models to predict sentiment expressed in a tweet on COVID-19 and compare their performances. The objective of the study is to gain insights into how the pandemic is affecting the population and to help policymakers and public health officials develop effective communication strategies and targeted interventions. By comparing the performances of these models, we hope to identify the most effective approach for classifying these sentiments, contributing to the broader conversation about the impacts of the pandemic on public sentiment and mental health.

Key-Words / Index Term :
COVID-19, social media, twitter, sentiment analysis, deep learning

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