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Since the emergence of the worldwide pandemic of COVID-19, relevant researchhas been published at a dazzling pace, which yields an abundant amount of bigdata in biomedical literature. Due to the high volum of relevant literature, itis practically impossible to follow up the research manually. Topic modeling isa well-known unsupervised learning that aims to reveal latent topics from textdata. In this paper, we propose a novel analytical framework for estimatingtopic interactions and effective visualization to improve topics'relationships. We first estimate topic-word distributions using the bitermtopic model and estimate the topics' interaction based on the word distributionusing the latent space item response model. We mapped these latent topics ontonetworks to visualize relationships among the topics. Moreover, in the proposedapproach, we developed a score that is helpful in selecting meaningful wordsthat characterize the topic. We figure out how topics are related by looking athow their relationships change. We do this with a "trajectory plot" that ismade with different levels of word richness. These findings provide athoroughly mined and intuitive representation of relationships between topicsrelated to a specific research area. The application of this proposed frameworkto the PubMed literature demonstrates utility of our approach in understandingof the topic composition related to COVID-19 studies in the stage of itsemergence.

Network-based Topic Interaction Map for Big Data Mining of COVID-19 Biomedical Literature