Semantic Topic Modeling of Aviation Safety Reports: A Comparative Analysis Using BERTopic and PLSA

Aviation safety analysis increasingly relies on extracting actionable insights from narrative incident reports to support risk identification and improve operational safety. Topic modeling techniques such as Probabilistic Latent Semantic Analysis (pLSA) and BERTopic offer automated methods to uncover latent themes in unstructured safety narratives. This study evaluates the effectiveness of each model in generating coherent, interpretable, and semantically meaningful topics for aviation safety practitioners and researchers. We assess model performance using both quantitative metrics (topic coherence scores) and qualitative evaluations of topic relevance. The findings show that while pLSA provides a solid probabilistic framework, BERTopic leveraging transformer-based embeddings and HDBSCAN clustering produces more nuanced, context-aware topic groupings, albeit with increased computational demands and tuning complexity. These results highlight the respective strengths and trade-offs of traditional versus modern topic modeling approaches in aviation safety analysis. This work advances the application of natural language processing (NLP) in aviation by demonstrating how topic modeling can support risk assessment, inform policy, and enhance safety outcomes.