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

arXiv: https://arxiv.org/abs/2005.10898 · HTML: https://mlsanity.com/paper/2005.10898/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2020.

## Built on by (most cited first, 11 of 11)

- [Sentiment Classification in Swahili Language Using Multilingual BERT](https://mlsanity.com/paper/2104.09006/) (2021)
- [Textual Data Distributions: Kullback Leibler Textual Distributions Contrasts on GPT-2 Generated Texts, with Supervised, Unsupervised Learning on Vaccine & Market Topics & Sentiment](https://mlsanity.com/paper/2107.02025/) (2021)
- [Adaptive cognitive fit: Artificial intelligence augmented management of information facets and representations](https://mlsanity.com/paper/2204.11405/) (2022)
- [A hybrid transformer and attention based recurrent neural network for robust and interpretable sentiment analysis of tweets](https://mlsanity.com/paper/2404.00297/) (2024)
- [Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review](https://mlsanity.com/paper/2104.04059/) (2021)
- [Event-driven timeseries analysis and the comparison of public reactions on COVID-19](https://mlsanity.com/paper/2104.14777/) (2021)
- [A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets](https://mlsanity.com/paper/2111.08658/) (2021)
- [COVID-19-related Nepali Tweets Classification in a Low Resource Setting](https://mlsanity.com/paper/2210.05425/) (2022)
- [The relationship between sentiment score and COVID-19 cases in the United States](https://mlsanity.com/paper/2202.01708/) (2022)
- [Covid-19 Public Sentiment Analysis for Indian Tweets Classification](https://mlsanity.com/paper/2308.06241/) (2023)
- [Identifying Risk Factors for Post-COVID-19 Mental Health Disorders: A Machine Learning Perspective](https://mlsanity.com/paper/2309.16055/) (2023)

## Abstract (arXiv)

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.