# Determination of the Number of Topics Intrinsically: Is It Possible?

arXiv: https://arxiv.org/abs/2406.10402 · HTML: https://mlsanity.com/paper/2406.10402/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 14 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Probabilistic Latent Semantic Analysis](https://mlsanity.com/paper/1301.6705/) (2013)
- [A network approach to topic models](https://mlsanity.com/paper/1708.01677/) (2017)
- [How Many Topics? Stability Analysis for Topic Models](https://mlsanity.com/paper/1404.4606/) (2014)
- [What is Wrong with Topic Modeling? (and How to Fix it Using Search-based Software Engineering)](https://mlsanity.com/paper/1608.08176/) (2016)
- [Stability of Topic Modeling via Matrix Factorization](https://mlsanity.com/paper/1702.07186/) (2017)
- [Measuring LDA Topic Stability from Clusters of Replicated Runs](https://mlsanity.com/paper/1808.08098/) (2018)
- [Fuzzy Approach Topic Discovery in Health and Medical Corpora](https://mlsanity.com/paper/1705.00995/) (2017)
- [Parsimonious Topic Models with Salient Word Discovery](https://mlsanity.com/paper/1401.6169/) (2014)
- [Prior matters: simple and general methods for evaluating and improving topic quality in topic modeling](https://mlsanity.com/paper/1701.03227/) (2017)
- [Application of R'enyi and Tsallis Entropies to Topic Modeling Optimization](https://mlsanity.com/paper/1802.10526/) (2018)
- ["Look Ma, No Hands!" A Parameter-Free Topic Model](https://mlsanity.com/paper/1409.2993/) (2014)

## Built on by (most cited first, 3 of 3)

- [Figurative Archive: an open dataset and web-based application for the study of metaphor](https://mlsanity.com/paper/2503.00444/) (2025)
- [Iterative Improvement of an Additively Regularized Topic Model](https://mlsanity.com/paper/2408.05840/) (2024)
- [Stick-Breaking Embedded Topic Model with Continuous Optimal Transport for Online Analysis of Document Streams](https://mlsanity.com/paper/2510.18786/) (2025)

## Abstract (arXiv)

The number of topics might be the most important parameter of a topic model. The topic modelling community has developed a set of various procedures to estimate the number of topics in a dataset, but there has not yet been a sufficiently complete comparison of existing practices. This study attempts to partially fill this gap by investigating the performance of various methods applied to several topic models on a number of publicly available corpora. Further analysis demonstrates that intrinsic methods are far from being reliable and accurate tools. The number of topics is shown to be a method- and a model-dependent quantity, as opposed to being an absolute property of a particular corpus. We conclude that other methods for dealing with this problem should be developed and suggest some promising directions for further research.