  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.
