# Comparative Analysis of Topic Modeling Techniques on ATSB Text Narratives Using Natural Language Processing

arXiv: https://arxiv.org/abs/2501.01227 · HTML: https://mlsanity.com/paper/2501.01227/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 24 references that are in the map.
- First posted on arXiv 2 Jan 2025.

## Builds on (most cited first, 1 of 1)

- [Probabilistic Latent Semantic Analysis](https://mlsanity.com/paper/1301.6705/) (2013)

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

- [Applications of natural language processing in aviation safety: A review and qualitative analysis](https://mlsanity.com/paper/2501.06210/) (2025)
- [Natural Language Processing and Deep Learning Models to Classify Phase of Flight in Aviation Safety Occurrences](https://mlsanity.com/paper/2501.06564/) (2025)
- [Analyzing Aviation Safety Narratives with LDA, NMF and PLSA: A Case Study Using Socrata Datasets](https://mlsanity.com/paper/2501.01690/) (2025)
- [Utilizing AI for Aviation Post-Accident Analysis Classification](https://mlsanity.com/paper/2506.00169/) (2025)

## Abstract (arXiv)

Improvements in aviation safety analysis call for innovative techniques to extract valuable insights from the abundance of textual data available in accident reports. This paper explores the application of four prominent topic modelling techniques, namely Probabilistic Latent Semantic Analysis (pLSA), Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and Non-negative Matrix Factorization (NMF), to dissect aviation incident narratives using the Australian Transport Safety Bureau (ATSB) dataset. The study examines each technique's ability to unveil latent thematic structures within the data, providing safety professionals with a systematic approach to gain actionable insights. Through a comparative analysis, this research not only showcases the potential of these methods in aviation safety but also elucidates their distinct advantages and limitations.