Topic Modelling (TM) is from the research branches of natural language understanding (NLU) and natural language processing (NLP) that is to facilitate insightful analysis from large documents and datasets, such as a summarisation of main topics and the topic changes. This kind of discovery is getting more popular in real-life applications due to its impact on big data analytics. In this study, from the social-media and healthcare domain, we apply popular Latent Dirichlet Allocation (LDA) methods to model the topic changes in Swedish newspaper articles about Coronavirus. We describe the corpus we created including 6515 articles, methods applied, and statistics on topic changes over approximately 1 year and two months period of time from 17th January 2020 to 13th March 2021. We hope this work can be an asset for grounding applications of topic modelling and can be inspiring for similar case studies in an era with pandemics, to support socio-economic impact research as well as clinical and healthcare analytics. Our data is openly available at https://github. com/poethan/Swed_Covid_TM Keywords: Latent Dirichlet Allocation (LDA); Topic Modelling; Coronavirus; Pandemics; Natural Language Understanding

该研究使用 Latent Dirichlet Allocation (LDA) 方法建模了瑞典报纸文章关于冠状病毒的主题变化，旨在支持社会经济影响研究以及临床和医疗保健分析等领域的话题建模应用。

使用潜在狄利克雷分配方法进行的关于冠状病毒的瑞典报纸文章主题建模： 以案例研究为基础

使用潜在狄利克雷分配方法进行的关于冠状病毒的瑞典报纸文章主题建模：以案例研究为基础