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1. Introduction
The coronavirus disease 2019 (COVID-19) pandemic has been affecting over 200 countries and territories across all regions. The pandemic is undoubtedly applying pressure on global manufacturer production capacities and logistics as an integral part of supply chains. Countries and companies have been taking various actions to protect the delivery of essential materials, including foods, medicines, masks and hazmat suits. While many countries have successfully managed the pandemic in phases, some logistics activities are gradually resuming to the prior pandemic scale.
Nonetheless, the crisis has led to rapid deterioration of business indicators, including GDP and productivity, and the impact on the world economy is predicted to be 2–3 times more severe than the world financial crisis in 2008–2009 (Harris, 2020). In the longer run, productivity is likely to be reduced by diminished R&D expenditure and diverted resource allocation of senior management to deal with the pandemic (Bloom et al., 2020). In addition, Brinca et al. (2020) measure the impact of COVID-19 on the labor demand and suggest that the demand for working hours decreased by 16% due to the imposition of travel and trade restrictions and the shutdown of working places. The authors conclude two-thirds of the fall in the growth rate of hours worked in March and April 2020. It could be attributed to adverse labor supply shocks, and it suggests that correctly measuring demand and supply shocks is essential for the design and implementation of economic policy during the COVID-19 outbreak.
Logistics companies, which are involved in the transport, storage and delivery of goods, have been directly affected by the COVID-19 pandemic. COVID-19 changed the way to connect between institutions, companies and individuals physically. However, the impact of COVID-19 on shipping and logistics is difficult to measure, and some of them are yet to be observed.
This research aims to gain insights through semantic analysis of Internet articles that discuss the impact of COVID-19 on shipping and logistics. This research presents a method of applying natural language processing (NLP) to extract Internet articles. The extracted text documents are then trained by machine learning (ML) algorithms which perform automatic text classification. We collected articles from Internet resources in viewing that the literature in this area is yet scarcely available in scientific journals....