mlsanityData to 14 Oct 2026

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

arXiv 2505.16493 ↗First posted 22 May 2025cs.LGcited by 0

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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.

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.

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21 of 66 references.

References · 21

  1. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
  2. 2017Autoencoding Variational Inference For Topic Modelscited by 169
  3. 2020Top2Vec: Distributed Representations of Topicscited by 126
  4. 2020Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherencecited by 121
  5. 2021Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherencecited by 74
  6. 2020Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!cited by 73
  7. 2020Cross-lingual Contextualized Topic Models with Zero-shot Learningcited by 67
  8. 2021Topic Modelling Meets Deep Neural Networks: A Surveycited by 51
  9. 2017Neural Models for Documents with Metadatacited by 46
  10. 2023Effective Neural Topic Modeling with Embedding Clustering Regularizationcited by 46
  11. 2024FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Modelcited by 35
  12. 2022TaxoCom: Topic Taxonomy Completion with Hierarchical Discovery of Novel Topic Clusterscited by 30
9 more
  1. 2016Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledgecited by 23
  2. 2021KKT Conditions, First-Order and Second-Order Optimization, and Distributed Optimization: Tutorial and Surveycited by 16
  3. 2022Effective Seed-Guided Topic Discovery by Integrating Multiple Types of Contextscited by 12
  4. 2024NeuroMax: Enhancing Neural Topic Modeling via Maximizing Mutual Information and Group Topic Regularizationcited by 9
  5. 2022Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generationcited by 7
  6. 2020Semi-supervised NMF Models for Topic Modeling in Learning Taskscited by 6
  7. 2014Discovering Structure in High-Dimensional Data Through Correlation Explanationcited by 5
  8. 2020On a Guided Nonnegative Matrix Factorizationcited by 5
  9. 2022Federated Non-negative Matrix Factorization for Short Texts Topic Modeling with Mutual Informationcited by 5

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