Determination of the Number of Topics Intrinsically: Is It Possible?
Not on the map. Why?
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 48 references that are in the map. First posted on arXiv 14 Jun 2024.
Builds on
11 of 48 references.
References · 11
- 2013Probabilistic Latent Semantic Analysiscited by 63
- 2017A network approach to topic modelscited by 25
- 2014How Many Topics? Stability Analysis for Topic Modelscited by 16
- 2016What is Wrong with Topic Modeling? (and How to Fix it Using Search-based Software Engineering)cited by 14
- 2017Stability of Topic Modeling via Matrix Factorizationcited by 13
- 2018Measuring LDA Topic Stability from Clusters of Replicated Runscited by 6
- 2017Fuzzy Approach Topic Discovery in Health and Medical Corporacited by 5
- 2014Parsimonious Topic Models with Salient Word Discoverycited by 2
- 2017Prior matters: simple and general methods for evaluating and improving topic quality in topic modelingcited by 2
- 2018Application of R'enyi and Tsallis Entropies to Topic Modeling Optimizationcited by 2
- 2014"Look Ma, No Hands!" A Parameter-Free Topic Modelcited by 1
Built on by
Citing papers per year: 2024: 1; 2025: 2.
3 papers.
- 2025Figurative Archive: an open dataset and web-based application for the study of metaphorcited by 4
- 2024Iterative Improvement of an Additively Regularized Topic Modelcited by 0
- 2025Stick-Breaking Embedded Topic Model with Continuous Optimal Transport for Online Analysis of Document Streamscited by 0
- 2025Stick-Breaking Embedded Topic Model with Continuous Optimal Transport for Online Analysis of Document Streams21 Oct 2025
- 2025Figurative Archive: an open dataset and web-based application for the study of metaphor1 Mar 2025
- 2024Iterative Improvement of an Additively Regularized Topic Model11 Aug 2024