Application of the LDA Model for Topic Identification: A Case Study on Dengue Diagnosis
DOI:
https://doi.org/10.15359/ru.39-1.19Keywords:
Text mining, Statistical learning, Text processing, Text recognition, LDA Model, DengueAbstract
[Objective]: This research aimed to identify and analyze topics in the scientific literature related to dengue, with a focus on diagnosis, signs, and symptoms using the Latent Dirichlet Assignment (LDA) model. [Methodology]: Articles were collected from various databases, including VHL, Web of Science, Ovid, Scopus, PubMed, Health & Medical, ScienceDirect, and Google Scholar, covering 2000-2024. The search equation was designed using key terms such as “dengue,” “signs,” “symptoms,” and “diagnosis,” along with MeSH terms to ensure the inclusion of relevant articles. The LDA model was then implemented to analyze the collected articles. [Results]: The implementation of the LDA model identified four main themes: 1) Dengue diagnosis and clinical presentation, 2) Research and control interventions, 3) Severe dengue and its clinical manifestations, and 4) Virus detection, including dengue, zika, and chikungunya. This thematic analysis facilitated the organization and understanding of literature, providing an overview of the predominant themes in dengue research. [Conclusions]: The study’s approach not only enhanced the organization and understanding of the articles found but also provided insights into the predominant themes in dengue literature, which may guide future research and improve diagnostic and treatment strategies.
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