COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms.

Ethical Considerations in the Application of Artificial Intelligence to Monitor Social Media for COVID-19 Data

“…Social media health research has shown promising potential and is being utilized for HIV surveillance (Heerden & Young, 2020), observation of opioid usage across the US , and to understand COVID-19 perceptions (Ugarte et al, 2021). The introduction of AI methods to COVID-19 social media research brings forth new ethical concerns and potential harms that require addressing (Ahmed et al, 2021;Jalil et al, 2022). Research principles from the Belmont Report may be utilized to establish best practices for utilizing social media data.…”

Modeling approaches for early warning and monitoring of pandemic situations as well as decision support

“…Various authors have also used social media to support the surveillance and monitoring of an epidemic (70)(71)(72). Missier et al (70) identified tweets related to dengue epidemics by classifying them into mosquito, sickness, and news-related classes.…”

“…These opinions from German-speaking countries were manually labeled, and Beck et al utilized predictions by transformer-based models. Jalil et al ( 71 ) performed sentiment analysis on tweets' text to classify them into positive, negative, and neutral. For the analysis, they used the COVIDSenti dataset ( 78 ) and reached the highest accuracy of 96.66% with the proposed Multi-depth DistilBERT method.…”

Social Networking Security during COVID-19: A Systematic Literature Review

“…The Covid-19 (Corona Virus) has been spread around all over the world now, and new cases and death rates are increasing day by day, which is an alarming situation for human life [5][6][7][8]. The Covid-19 pandemic shook the businesses, economy, trading, education [9,10], healthcare, especially the daily lifestyle of everyone globally [11][12][13].…”