Summary of An Iterative Approach to Topic Modelling, by Albert Wong et al.
An Iterative Approach to Topic Modelling
by Albert Wong, Florence Wing Yau Cheng, Ashley Keung, Yamileth Hercules, Mary Alexandra Garcia, Yew-Wei Lim, Lien Pham
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Topic modelling has become a popular method for summarizing large text datasets, including social media posts and articles. However, assessing the quality of resulting topics is challenging, and no effective methods or measures have been developed to evaluate the results or make further enhancements. This research proposes an iterative process for topic modelling that ensures a sense of completeness when the process is complete. The BERTopic package was used to demonstrate this approach, applying the modelling process iteratively using three selected measures for clustering comparison as the decision criteria. A subset of the COVIDSenti-A dataset was used for demonstration. This early success suggests that further research using this approach in conjunction with other topic modelling algorithms could be viable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a way to summarize huge amounts of text data, like social media posts or news articles, into smaller groups based on what they’re about. But right now, it’s hard to know if these summaries are any good or not. This research proposes a new approach that can be repeated until the results are as good as they can get. The researchers used a popular method called BERTopic and showed how this iterative process works by applying it to a subset of data from COVIDSenti-A. The early results look promising, suggesting that this approach could be useful for summarizing other types of text data. |
Keywords
* Artificial intelligence * Clustering
