# A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System

arXiv: https://arxiv.org/abs/2304.01774 · HTML: https://mlsanity.com/paper/2304.01774/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 4 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [Self-Taught Convolutional Neural Networks for Short Text Clustering](https://mlsanity.com/paper/1701.00185/) (2017)
- [Why Didn't You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models](https://mlsanity.com/paper/1905.09864/) (2019)
- [A Query-Driven Topic Model](https://mlsanity.com/paper/2106.07346/) (2021)

## Built on by (most cited first, 4 of 4)

- [Labeled Interactive Topic Models](https://mlsanity.com/paper/2311.09438/) (2023)
- [Effects of Collaboration on the Performance of Interactive Theme Discovery Systems](https://mlsanity.com/paper/2408.09030/) (2024)
- [Perspectives - Interactive Document Clustering in the Discourse Analysis Tool Suite](https://mlsanity.com/paper/2602.15540/) (2026)
- [Tackling the Root of Misinformation by Teaching Laypeople about Logical Fallacies via Socratic Questioning and Critical Argumentation](https://mlsanity.com/paper/2606.01020/) (2026)

## Abstract (arXiv)

Human-in-the-loop topic modelling incorporates users' knowledge into the modelling process, enabling them to refine the model iteratively. Recent research has demonstrated the value of user feedback, but there are still issues to consider, such as the difficulty in tracking changes, comparing different models and the lack of evaluation based on real-world examples of use. We developed a novel, interactive human-in-the-loop topic modeling system with a user-friendly interface that enables users compare and record every step they take, and a novel topic words suggestion feature to help users provide feedback that is faithful to the ground truth. Our system also supports not only what traditional topic models can do, i.e., learning the topics from the whole corpus, but also targeted topic modelling, i.e., learning topics for specific aspects of the corpus. In this article, we provide an overview of the system and present the results of a series of user studies designed to assess the value of the system in progressively more realistic applications of topic modelling.