Comparative Topic Modeling of Aviation Safety Narratives with NLP
An empirical comparison of four topic-modelling techniques - pLSA, LSA, LDA, and Non-negative Matrix Factorization - applied to free-text aviation incident narratives from the ATSB dataset. After NLP preprocessing and building a document-term matrix, each method extracts ten latent safety themes (bird strikes, landing gear, air traffic control, engine issues, and more), and the paper contrasts their interpretability, scalability, and sensitivity to preprocessing and hyperparameters to guide analysts choosing a method for large incident-report corpora.
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Comparative Analysis of Topic Modeling Techniques on ATSB Text Narratives Using Natural Language
A conference paper applying and comparing four topic-modelling techniques - Probabilistic Latent Semantic Analysis (pLSA), Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and Non-n…