Template-Type: ReDIF-Article 1.0
Title: A Machine-Learning Analysis of the Impacts of the COVID-19 Pandemic on Small Business Owners and Implications for Canadian Government Policy Response
Author-Name: Diane A. Isabelle
Author-Name: Yu (Jade) Han
Author-Name: Mika Westerlund
Abstract: This study applies a machine-learning technique to a dataset of 38,000 textual comments from Canadian small business owners on the impacts of coronavirus disease 2019 (COVID-19). Topic modelling revealed seven topics covering the short- and longer-term impacts of the pandemic, government relief programs and loan eligibility issues, mental health, and other impacts on business owners. The results emphasize the importance of policy response in aiding small business crisis management and offer implications for theory and policy. Moreover, the study provides an example of using a machine-learning-based automated content analysis in the fields of crisis management, small business, and public policy.
Classification-JEL: 
Keywords: COVID-19 crisis management, impacts, small business, topic modelling, Canada
Journal: Canadian Public Policy
Pages: 322-342
Volume: 48
Issue: 2
Year: 2022
Month: June
Paper-Handle: 
Publication-Status: Published
File-URL: http://dx.doi.org/10.3138/cpp.2021-018
File-Format: text/html
File-Restriction: access restricted to subscribers
Handle: RePEc:cpp:issued:v:48:y:2022:i:2:p:322-342