Coronavirus News, Markets and AI explores the analysis of unstructured data from coronavirus-related news and the underlying sentiment during its real-time impact on the world and global financial markets. In an age where real and fake information travels in the blink of an eye and significantly alters market sentiment daily, this book is a blow-by-blow account of the economic impact of the COVID-19 pandemic. The volume: Details how AI-driven machines capture, analyse and score relevant on-ground news sentiment to study the dynamics of market sentiment, how markets react to good or bad news across 'short term' and 'long term'; Investigates what has been the most prevalent news sentiment during the pandemic, and its linkages to crude oil prices, high profile cases, the impact of local news, and even the impact of Trump's policies; Discusses the effects on what people think and discuss, how the COVID-19 crisis differs from the Global Financial Crisis of 2008, the unprecedented disruptions in supply chains and our daily lives; Showcases how easy accessibility to extensive data methods, cloud computing, and computational methods and the universal applicability of these tool to any topic can help analyse extract the related news sentiment in allied fields. Accessible, nuanced and insightful, this book will be invaluable for business professionals, bankers, media professionals, traders, investors, and investment consultants. It will also greatly interest scholars and researchers of economics, commerce, science and technology studies, computer science, media and culture studies, public policy and digital humanities.