Exploring a Hybrid Deep Learning Framework to Automatically Discover Topic and Sentiment in COVID-19 Tweets

COVID-19 has created a major public health problem worldwide and other problems such as economic crisis, unemployment, mental distress, etc. The pandemic is deadly in the world and involves many people not only with infection but also with problems, stress, wonder, fear, resentment, and hatred. Twitter is a highly influential social media platform and a significant source of health-related information, news, opinion and public sentiment where information is shared by both citizens and government sources. Therefore an effective analysis of COVID-19 tweets is essential for policymakers to make wise decisions. However, it is challenging to identify interesting and useful content from major streams of text to understand people's feelings about the important topics of the COVID-19 tweets. In this paper, we propose a new \textit{framework} for analyzing topic-based sentiments by extracting key topics with significant labels and classifying positive, negative, or neutral tweets on each topic to quickly find common topics of public opinion and COVID-19-related attitudes. While building our model, we take into account hybridization of BiLSTM and GRU structures for sentiment analysis to achieve our goal. The experimental results show that our topic identification method extracts better topic labels and the sentiment analysis approach using our proposed hybrid deep learning model achieves the highest accuracy compared to traditional models.

A Dynamic Topic Identification and Labeling Approach of COVID-19 Tweets

This paper formulates the problem of dynamically identifying key topics with proper labels from COVID-19 Tweets to provide an overview of wider public opinion. Nowadays, social media is one of the best ways to connect people through Internet technology, which is also considered an essential part of our daily lives. In late December 2019, an outbreak of the novel coronavirus, COVID-19 was reported, and the World Health Organization declared an emergency due to its rapid spread all over the world. The COVID-19 epidemic has affected the use of social media by many people across the globe. Twitter is one of the most influential social media services, which has seen a dramatic increase in its use from the epidemic. Thus dynamic extraction of specific topics with labels from tweets of COVID-19 is a challenging issue for highlighting conversation instead of manual topic labeling approach. In this paper, we propose a framework that automatically identifies the key topics with labels from the tweets using the top Unigram feature of aspect terms cluster from Latent Dirichlet Allocation (LDA) generated topics. Our experiment result shows that this dynamic topic identification and labeling approach is effective having the accuracy of 85.48\% with respect to the manual static approach.