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 and source code are openly available at
https://github. com/poethan/Swed_Covid_TM Keywords: Latent Dirichlet Allocation
(LDA); Topic Modelling; Coronavirus; Pandemics; Natural Language Understanding;
BERT-topicComment: 14 pages, 14 figure