# Is BERTopic Better than PLSA for Extracting Key Topics in Aviation Safety Reports?

arXiv: https://arxiv.org/abs/2506.06328 · HTML: https://mlsanity.com/paper/2506.06328/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 30 May 2025.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Probabilistic Latent Semantic Analysis](https://mlsanity.com/paper/1301.6705/) (2013)
- [Applications of natural language processing in aviation safety: A review and qualitative analysis](https://mlsanity.com/paper/2501.06210/) (2025)

## Abstract (arXiv)

This study compares the effectiveness of BERTopic and Probabilistic Latent Semantic Analysis (PLSA) in extracting meaningful topics from aviation safety reports aiming to enhance the understanding of patterns in aviation incident data. Using a dataset of over 36,000 National Transportation Safety Board (NTSB) reports from 2000 to 2020, BERTopic employed transformer based embeddings and hierarchical clustering, while PLSA utilized probabilistic modelling through the Expectation-Maximization (EM) algorithm. Results showed that BERTopic outperformed PLSA in topic coherence, achieving a Cv score of 0.41 compared to PLSA 0.37, while also demonstrating superior interpretability as validated by aviation safety experts. These findings underscore the advantages of modern transformer based approaches in analyzing complex aviation datasets, paving the way for enhanced insights and informed decision-making in aviation safety. Future work will explore hybrid models, multilingual datasets, and advanced clustering techniques to further improve topic modelling in this domain.