Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

TL;DR

This paper employs NLP techniques, including LSTM neural networks and topic modeling, to analyze COVID-19 discussions on social media, revealing public concerns and sentiments to inform decision-making.

Contribution

It introduces a combined approach of topic modeling and LSTM-based sentiment analysis specifically applied to COVID-19 social media data, which is novel in this context.

Findings

Identified key issues discussed about COVID-19 on social media.

Classified sentiments of COVID-19 comments with high accuracy.

Highlighted the importance of computational analysis for health crisis management.

Abstract

Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19 related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making.