Isabelle Diane A, Han Yu Jade, Westerlund Mika
Sprott School of Business, Carleton University, Ottawa, Ontario, Canada, and Department of Business Management, School of Management, University of Johannesburg, Johannesburg, South Africa.
Faculty of Business Administration, University of Regina, Regina, Saskatchewan, Canada.
Can Public Policy. 2022 Jun 1;48(2):322-342. doi: 10.3138/cpp.2021-018.
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.
本研究将一种机器学习技术应用于一个数据集，该数据集包含来自加拿大小企业主关于2019冠状病毒病（COVID-19）影响的38000条文本评论。主题建模揭示了七个主题，涵盖了疫情的短期和长期影响、政府救助计划和贷款资格问题、心理健康以及对企业主的其他影响。研究结果强调了政策应对在帮助小企业危机管理中的重要性，并为理论和政策提供了启示。此外，该研究提供了一个在危机管理、小企业和公共政策领域使用基于机器学习的自动内容分析的示例。
