# Keyword Assisted Topic Models

arXiv: https://arxiv.org/abs/2004.05964 · HTML: https://mlsanity.com/paper/2004.05964/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2020.

## Built on by (most cited first, 12 of 12)

- [Keyword Assisted Embedded Topic Model](https://mlsanity.com/paper/2112.03101/) (2021)
- [Coordinated Topic Modeling](https://mlsanity.com/paper/2210.08559/) (2022)
- [Improving Probabilistic Models in Text Classification via Active Learning](https://mlsanity.com/paper/2202.02629/) (2022)
- [Analyzing Hong Kong's Legal Judgments from a Computational Linguistics point-of-view](https://mlsanity.com/paper/2305.02558/) (2023)
- [Multiclass Classification of Policy Documents with Large Language Models](https://mlsanity.com/paper/2310.08167/) (2023)
- [Using AI to Summarize US Presidential Campaign TV Advertisement Videos, 1952-2012](https://mlsanity.com/paper/2503.22589/) (2025)
- [E-LDA: Toward Interpretable LDA Topic Models with Strong Guarantees in Logarithmic Parallel Time](https://mlsanity.com/paper/2506.07747/) (2025)
- [A Comparison of Approaches for Imbalanced Classification Problems in the Context of Retrieving Relevant Documents for an Analysis](https://mlsanity.com/paper/2205.01600/) (2022)
- [CommunityFish: A Poisson-based Document Scaling With Hierarchical Clustering](https://mlsanity.com/paper/2308.14873/) (2023)
- [Seeded Poisson Factorization: leveraging domain knowledge to fit topic models](https://mlsanity.com/paper/2503.02741/) (2025)
- [Semi-supervised Vertex Hunting, with Applications in Network and Text Analysis](https://mlsanity.com/paper/2510.22526/) (2025)
- [LLM-generated personalized nudges for improving pro-environmental behavior: Field evidence from resource conservation](https://mlsanity.com/paper/2604.03881/) (2026)

## Abstract (arXiv)

In recent years, fully automated content analysis based on probabilistic topic models has become popular among social scientists because of their scalability. The unsupervised nature of the models makes them suitable for exploring topics in a corpus without prior knowledge. However, researchers find that these models often fail to measure specific concepts of substantive interest by inadvertently creating multiple topics with similar content and combining distinct themes into a single topic. In this paper, we empirically demonstrate that providing a small number of keywords can substantially enhance the measurement performance of topic models. An important advantage of the proposed keyword assisted topic model (keyATM) is that the specification of keywords requires researchers to label topics prior to fitting a model to the data. This contrasts with a widespread practice of post-hoc topic interpretation and adjustments that compromises the objectivity of empirical findings. In our application, we find that keyATM provides more interpretable results, has better document classification performance, and is less sensitive to the number of topics than the standard topic models. Finally, we show that keyATM can also incorporate covariates and model time trends. An open-source software package is available for implementing the proposed methodology.