COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

TL;DR

This study analyzes public sentiment on COVID-19 using Twitter data and machine learning, revealing fear trends over time and comparing classification methods for short and long Tweets.

Contribution

It introduces a comparative analysis of machine learning classifiers for COVID-19 Tweet sentiment, highlighting effectiveness differences based on Tweet length.

Findings

Naive Bayes achieves 91% accuracy on short Tweets

Logistic regression achieves 74% accuracy on short Tweets

Both methods perform weaker on longer Tweets

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.