COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification
Jim Samuel,G. G. Md. Nawaz Ali,Md. Mokhlesur Rahman,Md. Mokhlesur Rahman,Ek Esawi,Yana Samuel +5 more
TL;DR: This article identified public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages and demonstrated insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations.
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Abstract: Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19's informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naive Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.
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Citations
What social media told us in the time of COVID-19: a scoping review.
Shu-Feng Tsao,Helen H. Chen,Therese Tisseverasinghe,Yang Yang,Lianghua Li,Zahid A Butt +5 more
- 01 Mar 2021
TL;DR: In this paper, the authors identified five broad public health themes concerning the role of online social media platforms and COVID-19, focusing on: surveying public attitudes, identifying infodemics, assessing mental health, detecting or predicting COVID19 cases, analysing government responses to the pandemic, and evaluating quality of health information in prevention education videos.
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Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study.
Sakun Boon-itt,Yukolpat Skunkan +1 more
TL;DR: This study shows that Twitter is a good communication channel for understanding both public concern and public awareness about COVID-19, and can help health departments communicate information to alleviate specific public concerns about the disease.
Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers-A study to show how popularity is affecting accuracy in social media.
Koyel Chakraborty,Surbhi Bhatia,Siddhartha Bhattacharyya,Jan Platos,Rajib Bag,Aboul Ella Hassanien +5 more
- 01 Dec 2020
TL;DR: The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets.
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Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning on COVID-19 Related Tweets
TL;DR: Deep long short-term memory models used for estimating the sentiment polarity and emotions from extracted tweets have been trained to achieve state-of-the-art accuracy on the sentiment140 dataset and the use of emoticons showed a unique and novel way of validating the supervised deep learning models on tweets extracted from Twitter.
A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis.
TL;DR: In this paper, the authors performed Covid-19 tweets sentiment analysis using a supervised machine learning approach using a bag-of-words and the term frequency-inverse document frequency.
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