# Topic Modelling of Swedish Newspaper Articles about Coronavirus: a Case Study using Latent Dirichlet Allocation Method

arXiv: https://arxiv.org/abs/2301.03029 · HTML: https://mlsanity.com/paper/2301.03029/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 56 references that are in the map.
- First posted on arXiv 8 Jan 2023.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Top2Vec: Distributed Representations of Topics](https://mlsanity.com/paper/2008.09470/) (2020)
- [A network approach to topic models](https://mlsanity.com/paper/1708.01677/) (2017)
- [HOPE: A Task-Oriented and Human-Centric Evaluation Framework Using Professional Post-Editing Towards More Effective MT Evaluation](https://mlsanity.com/paper/2112.13833/) (2021)
- [Measuring Uncertainty in Translation Quality Evaluation (TQE)](https://mlsanity.com/paper/2111.07699/) (2021)
- [Chinese Character Decomposition for Neural MT with Multi-Word Expressions](https://mlsanity.com/paper/2104.04497/) (2021)
- [Meta-Evaluation of Translation Evaluation Methods: a systematic up-to-date overview](https://mlsanity.com/paper/1605.04515/) (2016)
- [An Overview on Machine Translation Evaluation](https://mlsanity.com/paper/2202.11027/) (2022)
- [Features of Perceived Metaphoricity on the Discourse Level: Abstractness and Emotionality](https://mlsanity.com/paper/2205.08939/) (2022)

## Built on by (most cited first, 2 of 2)

- [Neural Machine Translation of Clinical Text: An Empirical Investigation into Multilingual Pre-Trained Language Models and Transfer-Learning](https://mlsanity.com/paper/2312.07250/) (2023)
- [Analyzing Cancer Patients' Experiences with Embedding-based Topic Modeling and LLMs](https://mlsanity.com/paper/2601.12154/) (2026)

## Abstract (arXiv)

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-topic