Aspect Based Sentiment Analysis of COVID-19 Tweets Using Blending Ensemble of Deep Learning Models

Abstract

This paper takes into account the aspect-based sentiment analysis of COVID-19 tweets, in order to understand human emotions and provide decision support to policymakers. People these days use social media to share thoughts and feelings in critical situations like COVID-19. After the World Health Organization (WHO) declared COVID-19 a pandemic, a significant increase in the usage of the most influential Twitter platforms has been observed. Thus, it is impossible to manually track all the COVID-19-related tweets on the Twitter platform. Sentiment analysis is one of the solutions to this problem. In this work, we attempt to understand people’s feelings about certain aspects by analyzing the COVID-19 tweets to reduce the harmful consequences of the pandemic and to understand the crisis, humanitarian needs and measures. We, therefore, propose a framework for the aspect based sentiment analysis of COVID-19 tweets by extracting the top ten aspects and classifying positive, negative, or neutral tweets in each aspect using the blending ensemble of basic deep learning models. The experimental results show that the proposed framework achieves the highest accuracy of 85.65% compared to other benchmark deep learning models.