# Exploring Topic Trends in COVID-19 Research Literature using Non-Negative Matrix Factorization

arXiv: https://arxiv.org/abs/2503.18182 · HTML: https://mlsanity.com/paper/2503.18182/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 64 references that are in the map.
- First posted on arXiv 23 Mar 2025.

## Builds on (most cited first, 6 of 6)

- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [How Many Topics? Stability Analysis for Topic Models](https://mlsanity.com/paper/1404.4606/) (2014)
- [Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach](https://mlsanity.com/paper/1607.03055/) (2016)
- [Covid-19 Discourse on Twitter: How the Topics, Sentiments, Subjectivity, and Figurative Frames Changed Over Time](https://mlsanity.com/paper/2103.08952/) (2021)
- [A High-Performance Parallel Algorithm for Nonnegative Matrix Factorization](https://mlsanity.com/paper/1509.09313/) (2015)
- [Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks](https://mlsanity.com/paper/1606.00411/) (2016)

## Abstract (arXiv)

In this work, we apply topic modeling using Non-Negative Matrix Factorization (NMF) on the COVID-19 Open Research Dataset (CORD-19) to uncover the underlying thematic structure and its evolution within the extensive body of COVID-19 research literature. NMF factorizes the document-term matrix into two non-negative matrices, effectively representing the topics and their distribution across the documents. This helps us see how strongly documents relate to topics and how topics relate to words. We describe the complete methodology which involves a series of rigorous pre-processing steps to standardize the available text data while preserving the context of phrases, and subsequently feature extraction using the term frequency-inverse document frequency (tf-idf), which assigns weights to words based on their frequency and rarity in the dataset. To ensure the robustness of our topic model, we conduct a stability analysis. This process assesses the stability scores of the NMF topic model for different numbers of topics, enabling us to select the optimal number of topics for our analysis. Through our analysis, we track the evolution of topics over time within the CORD-19 dataset. Our findings contribute to the understanding of the knowledge structure of the COVID-19 research landscape, providing a valuable resource for future research in this field.