Global Sentiment Analysis Of COVID-19 Tweets Over Time

TL;DR

This study analyzes global sentiment trends on Twitter during COVID-19, examining how emotions evolved over time and assessing the impact of the pandemic on daily life topics using machine learning models for sentiment classification.

Contribution

It introduces a comprehensive analysis of COVID-19 related sentiments across countries and evaluates machine learning models for sentiment classification on pandemic-related tweets.

Findings

Sentiment varied significantly over time and across countries.

LSTM and ANN models achieved high accuracy in classifying sentiments.

Sentiment changes correlated with the number of confirmed cases.

Abstract

The Coronavirus pandemic has affected the normal course of life. People around the world have taken to social media to express their opinions and general emotions regarding this phenomenon that has taken over the world by storm. The social networking site, Twitter showed an unprecedented increase in tweets related to the novel Coronavirus in a very short span of time. This paper presents the global sentiment analysis of tweets related to Coronavirus and how the sentiment of people in different countries has changed over time. Furthermore, to determine the impact of Coronavirus on daily aspects of life, tweets related to Work From Home (WFH) and Online Learning were scraped and the change in sentiment over time was observed. In addition, various Machine Learning models such as Long Short Term Memory (LSTM) and Artificial Neural Networks (ANN) were implemented for sentiment classification and their accuracies were determined. Exploratory data analysis was also performed for a dataset providing information about the number of confirmed cases on a per-day basis in a few of the worst-hit countries to provide a comparison between the change in sentiment with the change in cases since the start of this pandemic till June 2020.