# Enhance Topics Analysis based on Keywords Properties

arXiv: https://arxiv.org/abs/2203.04786 · HTML: https://mlsanity.com/paper/2203.04786/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Improving Topic Models with Latent Feature Word Representations](https://mlsanity.com/paper/1810.06306/) (2018)
- [Coherence-Aware Neural Topic Modeling](https://mlsanity.com/paper/1809.02687/) (2018)
- [An Automatic Approach for Document-level Topic Model Evaluation](https://mlsanity.com/paper/1706.05140/) (2017)

## Built on by (most cited first, 1 of 1)

- [T5Score: A Methodology for Automatically Assessing the Quality of LLM Generated Multi-Document Topic Sets](https://mlsanity.com/paper/2407.17390/) (2024)

## Abstract (arXiv)

Topic Modelling is one of the most prevalent text analysis technique used to explore and retrieve collection of documents. The evaluation of the topic model algorithms is still a very challenging tasks due to the absence of gold-standard list of topics to compare against for every corpus. In this work, we present a specificity score based on keywords properties that is able to select the most informative topics. This approach helps the user to focus on the most informative topics. In the experiments, we show that we are able to compress the state-of-the-art topic modelling results of different factors with an information loss that is much lower than the solution based on the recent coherence score presented in literature.