# Holistic Evaluations of Topic Models

arXiv: https://arxiv.org/abs/2507.23364 · HTML: https://mlsanity.com/paper/2507.23364/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 31 Jul 2025.

## Builds on (most cited first, 3 of 3)

- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Multilingual transformer and BERTopic for short text topic modeling: The case of Serbian](https://mlsanity.com/paper/2402.03067/) (2024)
- [BERTDetect: A Neural Topic Modelling Approach for Android Malware Detection](https://mlsanity.com/paper/2503.18043/) (2025)

## Abstract (arXiv)

Topic models are gaining increasing commercial and academic interest for their ability to summarize large volumes of unstructured text. As unsupervised machine learning methods, they enable researchers to explore data and help general users understand key themes in large text collections. However, they risk becoming a 'black box', where users input data and accept the output as an accurate summary without scrutiny. This article evaluates topic models from a database perspective, drawing insights from 1140 BERTopic model runs. The goal is to identify trade-offs in optimizing model parameters and to reflect on what these findings mean for the interpretation and responsible use of topic models