Abstract
In the present study Latent Dirichlet allocation (LDA) was used as a generative probabilistic model to extract major topics in interdecadalresearch for the Veterinary Medicine scientific literature. A total of 22 topics were extracted during the 1991-2000 period, 23 topics during 2001-2010 and 60 topics during 2011-2020. Three different algorithms were used to validate the model: perplexity, silhouette clustering and gradient boosted trees. All three validation metrics showed that LDA performed well in extracting topics. Each decade was characterized by unique topics as well as common topics which existed throughout periods. The most frequent topics were identified and trends were quantified with the use of indexes. A list of the 30 most frequent and most associated with the term Veterinary Medicine words is provided. A shift in scientific thinking probably occurred during the 30-year-period in the process of incorporating the fields related to Veterinary students, antimicrobial resistance and animals’ behavior.
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