# Constrained Non-negative Matrix Factorization for Guided Topic Modeling of Minority Topics

arXiv: https://arxiv.org/abs/2505.16493 · HTML: https://mlsanity.com/paper/2505.16493/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 66 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 21 of 21)

- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Autoencoding Variational Inference For Topic Models](https://mlsanity.com/paper/1703.01488/) (2017)
- [Top2Vec: Distributed Representations of Topics](https://mlsanity.com/paper/2008.09470/) (2020)
- [Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence](https://mlsanity.com/paper/2004.03974/) (2020)
- [Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence](https://mlsanity.com/paper/2107.02173/) (2021)
- [Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!](https://mlsanity.com/paper/2004.14914/) (2020)
- [Cross-lingual Contextualized Topic Models with Zero-shot Learning](https://mlsanity.com/paper/2004.07737/) (2020)
- [Topic Modelling Meets Deep Neural Networks: A Survey](https://mlsanity.com/paper/2103.00498/) (2021)
- [Neural Models for Documents with Metadata](https://mlsanity.com/paper/1705.09296/) (2017)
- [Effective Neural Topic Modeling with Embedding Clustering Regularization](https://mlsanity.com/paper/2306.04217/) (2023)
- [FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model](https://mlsanity.com/paper/2405.17978/) (2024)
- [TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusters](https://mlsanity.com/paper/2201.06771/) (2022)
- [Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge](https://mlsanity.com/paper/1611.10277/) (2016)
- [KKT Conditions, First-Order and Second-Order Optimization, and Distributed Optimization: Tutorial and Survey](https://mlsanity.com/paper/2110.01858/) (2021)
- [Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contexts](https://mlsanity.com/paper/2212.06002/) (2022)
- [NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularization](https://mlsanity.com/paper/2409.19749/) (2024)
- [Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation](https://mlsanity.com/paper/2211.01981/) (2022)
- [Semi-supervised NMF Models for Topic Modeling in Learning Tasks](https://mlsanity.com/paper/2010.07956/) (2020)
- [Discovering Structure in High-Dimensional Data Through Correlation Explanation](https://mlsanity.com/paper/1406.1222/) (2014)
- [On a Guided Nonnegative Matrix Factorization](https://mlsanity.com/paper/2010.11365/) (2020)
- [Federated Non-negative Matrix Factorization for Short Texts Topic Modeling with Mutual Information](https://mlsanity.com/paper/2205.13300/) (2022)

## Abstract (arXiv)

Topic models often fail to capture low-prevalence, domain-critical themes, so-called minority topics, such as mental health themes in online comments. While some existing methods can incorporate domain knowledge, such as expected topical content, methods allowing guidance may require overly detailed expected topics, hindering the discovery of topic divisions and variation. We propose a topic modeling solution via a specially constrained NMF. We incorporate a seed word list characterizing minority content of interest, but we do not require experts to pre-specify their division across minority topics. Through prevalence constraints on minority topics and seed word content across topics, we learn distinct data-driven minority topics as well as majority topics. The constrained NMF is fitted via Karush-Kuhn-Tucker (KKT) conditions with multiplicative updates. We outperform several baselines on synthetic data in terms of topic purity, normalized mutual information, and also evaluate topic quality using Jensen-Shannon divergence (JSD). We conduct a case study on YouTube vlog comments, analyzing viewer discussion of mental health content; our model successfully identifies and reveals this domain-relevant minority content.