# A Query-Driven Topic Model

arXiv: https://arxiv.org/abs/2106.07346 · HTML: https://mlsanity.com/paper/2106.07346/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 28 May 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Autoencoding Variational Inference For Topic Models](https://mlsanity.com/paper/1703.01488/) (2017)
- [Self-Taught Convolutional Neural Networks for Short Text Clustering](https://mlsanity.com/paper/1701.00185/) (2017)
- [Query Expansion with Locally-Trained Word Embeddings](https://mlsanity.com/paper/1605.07891/) (2016)
- [Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections](https://mlsanity.com/paper/1908.00475/) (2019)
- [Topic Modelling of Everyday Sexism Project Entries](https://mlsanity.com/paper/1711.09074/) (2017)

## Built on by (most cited first, 5 of 5)

- [A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System](https://mlsanity.com/paper/2304.01774/) (2023)
- [A Novel, Human-in-the-Loop Computational Grounded Theory Framework for Big Social Data](https://mlsanity.com/paper/2506.06083/) (2025)
- [Using Computational Grounded Theory to Understand Tutors' Experiences in the Gig Economy](https://mlsanity.com/paper/2201.09787/) (2022)
- [SyROCCo: Enhancing Systematic Reviews using Machine Learning](https://mlsanity.com/paper/2406.16527/) (2024)
- [Effects of Collaboration on the Performance of Interactive Theme Discovery Systems](https://mlsanity.com/paper/2408.09030/) (2024)

## Abstract (arXiv)

Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus. It has been increasingly widely adopted as a tool in the social sciences, including political science, digital humanities and sociological research in general. One desirable property of topic models is to allow users to find topics describing a specific aspect of the corpus. A possible solution is to incorporate domain-specific knowledge into topic modeling, but this requires a specification from domain experts. We propose a novel query-driven topic model that allows users to specify a simple query in words or phrases and return query-related topics, thus avoiding tedious work from domain experts. Our proposed approach is particularly attractive when the user-specified query has a low occurrence in a text corpus, making it difficult for traditional topic models built on word cooccurrence patterns to identify relevant topics. Experimental results demonstrate the effectiveness of our model in comparison with both classical topic models and neural topic models.