Deep Sentiment Classification in COVID-19 Using LSTM Recurrent Neural Network

Preview this chapter:

Deep Sentiment Classification in COVID-19 Using LSTM Recurrent Neural Network, Page 1 of 1

< Previous page | Next page > /docserver/preview/fulltext/9789815305395/chapter-41-1.gif

Users (people/patients) concerned about health concerns have an easy outlet in online medical forums along with other public social media on the Internet. The World Health Organization declared a global public health emergency in response to the emergence of a new coronavirus (infection which causes the disease termed COVID-19) in late December 2019. In this research, we employed a natural language processing (NLP) technique based on topic modeling to automatically extract COVID19-related talks from social media and discover numerous concerns linked to COVID19 from public viewpoints. As an added bonus, we look into the possibility of employing a long short-term memory (LSTM) recurrent neural network to accomplish the same task with COVID-19 remarks. Our research highlights the value of incorporating public opinion and appropriate computational approaches into the process of learning about and making decisions on COVID-19. The trials also showed that the study model was able to reach an accuracy of 81.15 percent, which is greater than the accuracy attained by many other popular machine-learning methods for COVID-19 sentiment classification.