Sentiment Analysis of Covid-19 Tweets using Evolutionary Classification-Based LSTM Model

TL;DR

This paper presents an evolutionary classification-based LSTM model for analyzing public sentiment on Covid-19 tweets, achieving an accuracy of 84.46% in sentiment prediction during the pandemic.

Contribution

It introduces a novel combination of evolutionary classification and LSTM for sentiment analysis of Covid-19 tweets, including trend analysis and sentiment rating.

Findings

Achieved 84.46% overall sentiment prediction accuracy.

Analyzed public sentiment trends related to Covid-19.

Developed a hybrid approach combining n-gram analysis and LSTM.

Abstract

As the Covid-19 outbreaks rapidly all over the world day by day and also affects the lives of million, a number of countries declared complete lock-down to check its intensity. During this lockdown period, social media plat-forms have played an important role to spread information about this pandemic across the world, as people used to express their feelings through the social networks. Considering this catastrophic situation, we developed an experimental approach to analyze the reactions of people on Twitter taking into ac-count the popular words either directly or indirectly based on this pandemic. This paper represents the sentiment analysis on collected large number of tweets on Coronavirus or Covid-19. At first, we analyze the trend of public sentiment on the topics related to Covid-19 epidemic using an evolutionary classification followed by the n-gram analysis. Then we calculated the sentiment ratings on collected tweet based on their class. Finally, we trained the long-short term network using two types of rated tweets to predict sentiment on Covid-19 data and obtained an overall accuracy of 84.46%.