# Topic Modeling based on Keywords and Context

arXiv: https://arxiv.org/abs/1710.02650 · HTML: https://mlsanity.com/paper/1710.02650/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2017.

## Built on by (most cited first, 3 of 3)

- [Topic Modeling with Fine-tuning LLMs and Bag of Sentences](https://mlsanity.com/paper/2408.03099/) (2024)
- [Efficient and Flexible Topic Modeling using Pretrained Embeddings and Bag of Sentences](https://mlsanity.com/paper/2302.03106/) (2023)
- [Thematic and Task-Based Categorization of K-12 GenAI Usages with Hierarchical Topic Modeling](https://mlsanity.com/paper/2508.09997/) (2025)

## Abstract (arXiv)

Current topic models often suffer from discovering topics not matching human intuition, unnatural switching of topics within documents and high computational demands. We address these concerns by proposing a topic model and an inference algorithm based on automatically identifying characteristic keywords for topics. Keywords influence topic-assignments of nearby words. Our algorithm learns (key)word-topic scores and it self-regulates the number of topics. Inference is simple and easily parallelizable. Qualitative analysis yields comparable results to state-of-the-art models (eg. LDA), but with different strengths and weaknesses. Quantitative analysis using 9 datasets shows gains in terms of classification accuracy, PMI score, computational performance and consistency of topic assignments within documents, while most often using less topics.