Abstract
The COVID-19 pandemic has hit the world hard. The reaction to the pandemic related issues has been pouring into social platforms, such as Twitter. Many public officials and governments use Twitter to make policy announcements. People keep close track of the related information and express their concerns about the policies on Twitter. It is beneficial yet challenging to derive important information or knowledge out of such Twitter data. In this paper, we propose a Tripartite Graph Clustering for Pandemic Data Analysis (TGC-PDA) framework that builds on the proposed models and analysis: (1) tripartite graph representation, (2) non-negative matrix factorization with regularization, and (3) sentiment analysis. We collect the tweets containing a set of keywords related to coronavirus pandemic as the ground truth data. Our framework can detect the communities of Twitter users and analyze the topics that are discussed in the communities. The extensive experiments show that our TGC-PDA framework can effectively and efficiently identify the topics and correlations within the Twitter data for monitoring and understanding public opinions, which would provide policy makers useful information and statistics for decision making.
Highlights
The COVID-19 has hit the world and made major impacts in society, economy, medical care, environment, and so on[1,2]
We investigate the issue of pandemic analysis through Twitter data, and propose a framework of Tripartite Graph Clustering for Pandemic Data Analysis (TGC-PDA)
5 negative Matrix Factorization with Regularization (NMFR) Updating Algorithm we focus on solving the objective function that is formulated as a minimization problem
Summary
The COVID-19 has hit the world and made major impacts in society, economy, medical care, environment, and so on[1,2]. To prevent the virus from spreading all over the world, unnecessary travel has been restricted, quarantines are required, and even Tokyo Olympics was postponed[3,4,5]. The virus has been spreading to almost the whole world. RNA significantly changes its structure[6]. This means the vaccine may need to be constantly updated and there could be a lengthy recovery for many people. A multipartite graph is usually used to model heterogeneous data. When k D 3, a k-partite graph is called a tripartite graph, which generally means it is constructed using data from three heterogeneous sources
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