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

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...

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Bibliographic Details
Published in2024 3rd International Conference for Innovation in Technology (INOCON) pp. 1 - 7
Main Authors Nanyonga, Aziida, Wasswa, Hassan, Turhan, Ugur, Joiner, Keith, Wild, Graham
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2024
Subjects
Analytical models
ATSB
Aviation Safety
Data models
Natural language processing
NLP
Predictive models
Semantics
Systematics
Technological innovation
Topic Modelling
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Summary: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.
DOI:10.1109/INOCON60754.2024.10511951