Sentiment Analysis of Microblogging dataset on Coronavirus Pandemic

TL;DR

This paper analyzes Twitter data related to COVID-19 to classify sentiments and understand the context, aiding in spreading accurate information and awareness during the pandemic.

Contribution

It applies multiple machine learning algorithms to classify COVID-19 related sentiments and explores context learning from the dataset, which is a novel approach in this domain.

Findings

Effective sentiment classification achieved with machine learning algorithms

Identified positive, negative, and neutral sentiments in COVID-19 tweets

Analyzed the contextual meaning of sentiments in the dataset

Abstract

Sentiment analysis can largely influence the people to get the update of the current situation. Coronavirus (COVID-19) is a contagious illness caused by the coronavirus 2 that causes severe respiratory symptoms. The lives of millions have continued to be affected by this pandemic, several countries have resorted to a full lockdown. During this lockdown, people have taken social networks to express their emotions to find a way to calm themselves down. People are spreading their sentiments through microblogging websites as one of the most preventive steps of this disease is the socialization to gain people's awareness to stay home and keep their distance when they are outside home. Twitter is a popular online social media platform for exchanging ideas. People can post their different sentiments, which can be used to aware people. But, some people want to spread fake news to frighten the people. So, it is necessary to identify the positive, negative, and neutral thoughts so that the positive opinions can be delivered to the mass people for spreading awareness to the people. Moreover, a huge volume of data is floating on Twitter. So, it is also important to identify the context of the dataset. In this paper, we have analyzed the Twitter dataset for evaluating the sentiment using several machine learning algorithms. Later, we have found out the context learning of the dataset based on the sentiments.