# An Iterative Approach to Topic Modelling

arXiv: https://arxiv.org/abs/2407.17892 · HTML: https://mlsanity.com/paper/2407.17892/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 25 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Top2Vec: Distributed Representations of Topics](https://mlsanity.com/paper/2008.09470/) (2020)
- [Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey](https://mlsanity.com/paper/1711.04305/) (2017)
- [Topic Modeling with Contextualized Word Representation Clusters](https://mlsanity.com/paper/2010.12626/) (2020)
- [Short Text Topic Modeling Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1904.07695/) (2019)
- [Text Similarity in Vector Space Models: A Comparative Study](https://mlsanity.com/paper/1810.00664/) (2018)
- [Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter](https://mlsanity.com/paper/2004.06986/) (2020)
- [Comparison of Topic Modelling Approaches in the Banking Context](https://mlsanity.com/paper/2402.03176/) (2024)

## Abstract (arXiv)

Topic modelling has become increasingly popular for summarizing text data, such as social media posts and articles. However, topic modelling is usually completed in one shot. Assessing the quality of resulting topics is challenging. No effective methods or measures have been developed for assessing the results or for making further enhancements to the topics. In this research, we propose we propose to use an iterative process to perform topic modelling that gives rise to a sense of completeness of the resulting topics when the process is complete. Using the BERTopic package, a popular method in topic modelling, we demonstrate how the modelling process can be applied iteratively to arrive at a set of topics that could not be further improved upon using one of the three selected measures for clustering comparison as the decision criteria. This demonstration is conducted using a subset of the COVIDSenti-A dataset. The early success leads us to believe that further research using in using this approach in conjunction with other topic modelling algorithms could be viable.