An Iterative Approach to Topic Modelling

TL;DR

This paper introduces an iterative method for topic modelling that enhances the quality and completeness of topics by applying multiple rounds of refinement, demonstrated with BERTopic on COVID-19 social media data.

Contribution

It proposes an iterative process for topic modelling that improves topic quality and assessment, filling a gap in existing one-shot methods.

Findings

Iterative approach yields more complete and refined topics.

Using clustering measures to determine when to stop iteration.

Successful application on COVIDSenti-A dataset.

Abstract

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.