# Evaluating topic coherence measures

arXiv: https://arxiv.org/abs/1403.6397 · HTML: https://mlsanity.com/paper/1403.6397/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2014.

## Built on by (most cited first, 6 of 6)

- [From Chatbots to PhishBots? -- Preventing Phishing scams created using ChatGPT, Google Bard and Claude](https://mlsanity.com/paper/2310.19181/) (2023)
- [Topic Modeling on Clinical Social Work Notes for Exploring Social Determinants of Health Factors](https://mlsanity.com/paper/2212.01462/) (2022)
- [Bridging the gap between supervised classification and unsupervised topic modelling for social-media assisted crisis management](https://mlsanity.com/paper/2103.11835/) (2021)
- [From Stars to Insights: Exploration and Implementation of Unified Sentiment Analysis with Distant Supervision](https://mlsanity.com/paper/2305.01710/) (2023)
- [MultiPhishGuard: An Explainable and Adaptive Multi-Agent LLM System for Phishing Email Detection](https://mlsanity.com/paper/2505.23803/) (2025)
- [Enhancing Online Support Group Formation Using Topic Modeling Techniques](https://mlsanity.com/paper/2603.24765/) (2026)

## Abstract (arXiv)

Topic models extract representative word sets - called topics - from word counts in documents without requiring any semantic annotations. Topics are not guaranteed to be well interpretable, therefore, coherence measures have been proposed to distinguish between good and bad topics. Studies of topic coherence so far are limited to measures that score pairs of individual words. For the first time, we include coherence measures from scientific philosophy that score pairs of more complex word subsets and apply them to topic scoring.