# How Many Topics? Stability Analysis for Topic Models

arXiv: https://arxiv.org/abs/1404.4606 · HTML: https://mlsanity.com/paper/1404.4606/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2014.

## Built on by (most cited first, 16 of 16)

- [Prototypical Human-AI Collaboration Behaviors from LLM-Assisted Writing in the Wild](https://mlsanity.com/paper/2505.16023/) (2025)
- [Are Neural Topic Models Broken?](https://mlsanity.com/paper/2210.16162/) (2022)
- [Determination of the Number of Topics Intrinsically: Is It Possible?](https://mlsanity.com/paper/2406.10402/) (2024)
- [Jensen-Shannon Divergence Message-Passing for Rich-Text Graph Representation Learning](https://mlsanity.com/paper/2512.20094/) (2025)
- [Topic Model Robustness to Automatic Speech Recognition Errors in Podcast Transcripts](https://mlsanity.com/paper/2109.12306/) (2021)
- [Discovering Significant Topics from Legal Decisions with Selective Inference](https://mlsanity.com/paper/2401.01068/) (2024)
- [Topic Modelling: Going Beyond Token Outputs](https://mlsanity.com/paper/2401.12990/) (2024)
- [SinhaLegal: A Benchmark Corpus for Information Extraction and Analysis in Sinhala Legislative Texts](https://mlsanity.com/paper/2603.04854/) (2026)
- [Exclusive Topic Modeling](https://mlsanity.com/paper/2102.03525/) (2021)
- [Changepoint Analysis of Topic Proportions in Temporal Text Data](https://mlsanity.com/paper/2112.00827/) (2021)
- [The study of short texts in digital politics: Document aggregation for topic modeling](https://mlsanity.com/paper/2503.05065/) (2025)
- [Exploring Topic Trends in COVID-19 Research Literature using Non-Negative Matrix Factorization](https://mlsanity.com/paper/2503.18182/) (2025)
- [The Differential Meaning of Models: A Framework for Analyzing the Structural Consequences of Semantic Modeling Decisions](https://mlsanity.com/paper/2509.00248/) (2025)
- [Repeatability is not recovery: Quantifying algorithmic stability and topic recovery in Latent Dirichlet Allocation](https://mlsanity.com/paper/2511.12850/) (2025)
- [Hybrid topic modelling for computational close reading: Mapping narrative themes in Pushkin's Evgenij Onegin](https://mlsanity.com/paper/2603.19940/) (2026)
- [CobwebTM: Probabilistic Concept Formation for Lifelong and Hierarchical Topic Modeling](https://mlsanity.com/paper/2604.14489/) (2026)

## Abstract (arXiv)

Topic modeling refers to the task of discovering the underlying thematic structure in a text corpus, where the output is commonly presented as a report of the top terms appearing in each topic. Despite the diversity of topic modeling algorithms that have been proposed, a common challenge in successfully applying these techniques is the selection of an appropriate number of topics for a given corpus. Choosing too few topics will produce results that are overly broad, while choosing too many will result in the "over-clustering" of a corpus into many small, highly-similar topics. In this paper, we propose a term-centric stability analysis strategy to address this issue, the idea being that a model with an appropriate number of topics will be more robust to perturbations in the data. Using a topic modeling approach based on matrix factorization, evaluations performed on a range of corpora show that this strategy can successfully guide the model selection process.