The Latent Dirichlet Allocation Model: Foundations, Trends, and Challenges
DOI:
https://doi.org/10.22201/fesa.29928273e.2026.12.103Keywords:
Latent Dirichlet Allocation, machine learning, thematic modeling, Web of Knowledge, bibliometric analysis.Abstract
This article presents research progress on the theoretical framework and state of the art for a master’s level engineering thesis that proposes an open cloud-based tool which enables users without advanced technical or programming knowledge to conduct topic modeling projects based on the Latent Dirichlet Allocation algorithm. To this end, the theoretical foundations are described, and a bibliometric analysis is performed using information from 2,113 documents published between 2002 and 2024, retrieved from the Web of Knowledge platform, in order to identify trends, applications, and challenges. A growing use of this model is highlitened, because its accuracy and reliability. Furthermore, its implementation in social science and humanities is identified as a relevant area of opportunity to promote its use in interdisciplinary contexts.
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