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.

This paper presents the results of the first application of BERTopic, a state-of-the-art topic modeling technique, to short text written in a morphologi-cally rich language. We applied BERTopic with three multilingual embed-ding models on two levels of text preprocessing (partial and full) to evalu-ate its performance on partially preprocessed short text in Serbian. We also compared it to LDA and NMF on fully preprocessed text. The experiments were conducted on a dataset of tweets expressing hesitancy toward COVID-19 vaccination. Our results show that with adequate parameter setting, BERTopic can yield informative topics even when applied to partially pre-processed short text. When the same parameters are applied in both prepro-cessing scenarios, the performance drop on partially preprocessed text is minimal. Compared to LDA and NMF, judging by the keywords, BERTopic offers more informative topics and gives novel insights when the number of topics is not limited. The findings of this paper can be significant for re-searchers working with other morphologically rich low-resource languages and short text.

Numerical combination of LDA and NMF cascaded with W2V to categorize 1M multi-lingual records into a 275-node, 5-level deep category tree. Mixed were the feelings when I got to know the Top 3 position in Hackathon was a narrow miss by 1 mark. While it was kinda re-assuring to stand 4th among Finalist Teams, despite myself participating alone, the void of one passionate team partner, was quite disquieting. The Hackathon challenge was to multi-categorize 1M multi-lingual articles with meta-information, at high precision. The meta-information denotes the category-tree information hidden in the URL text sequence and also the title of each article.

Latent Dirichlet Allocation (LDA) is a algorithms used to discover the topics that are present in a corpus. A few open source libraries exist, but if you are using Python then the main contender is Gensim. Gensim is an awesome library and scales really well to large text corpuses. Gensim, however does not include Non-negative Matrix Factorization (NMF), which can also be used to find topics in text. The mathematical basis underpinning NMF is quite different from LDA.