Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is wreaking havoc. This virus has infected more than 62.01 million and killed around 1.44 million people worldwide in less than a year. For the past 11 months, this is the most critical issue that the world is dealing with. Hence, there is a rapid accumulation of coronavirus-related news. Natural language processing (NLP) and machine learning (ML) methods such as topic modeling receive much attention because of their ability to discover hidden themes and issues from large unstructured text data. We collected 63,424 COVID-19/coronavirus themed news articles from Japanese and Indian English newspapers and applied the recently proposed Top2Vec model to analyze and extract major topics. Our research finds out that both countries’ media reported heavily about the problems that arise due to coronavirus in sports, education, and entertainment sectors. Our findings also point out that Indian media gave very little space to the issues such as unemployment and the migrant crisis that impacted millions during this period. This research can be used as a template to understand and analyze how this pandemic impacted other countries. It also brought to our attention the media’s failure to prioritize critical importance issues for society (migrant crisis) and focused on trivial news (celebrities social media posts).