# Topic Modeling Analysis of Aviation Accident Reports: A Comparative Study between LDA and NMF Models

arXiv: https://arxiv.org/abs/2403.04788 · HTML: https://mlsanity.com/paper/2403.04788/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 21 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## 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)
- [Classification of Operational Records in Aviation Using Deep Learning Approaches](https://mlsanity.com/paper/2501.01222/) (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)

Aviation safety is paramount in the modern world, with a continuous commitment to reducing accidents and improving safety standards. Central to this endeavor is the analysis of aviation accident reports, rich textual resources that hold insights into the causes and contributing factors behind aviation mishaps. This paper compares two prominent topic modeling techniques, Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), in the context of aviation accident report analysis. The study leverages the National Transportation Safety Board (NTSB) Dataset with the primary objective of automating and streamlining the process of identifying latent themes and patterns within accident reports. The Coherence Value (C_v) metric was used to evaluate the quality of generated topics. LDA demonstrates higher topic coherence, indicating stronger semantic relevance among words within topics. At the same time, NMF excelled in producing distinct and granular topics, enabling a more focused analysis of specific aspects of aviation accidents.