Search and classify topics in a corpus of text using the latent dirichlet allocation model

• Autores: Iparraguirre-Villanueva, Orlando, Sierra-Liñan, Fernando, Herrera Salazar, Jose Luis, Beltozar-Clemente, Saul, Pucuhuayla-Revatta, Félix, Zapata-Paulini, Joselyn, Cabanillas-Carbonell, Michael
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad Autónoma del Perú
• Repositorio: AUTONOMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.autonoma.edu.pe:20.500.13067/2829
• Enlace del recurso: https://hdl.handle.net/20.500.13067/2829; https://doi.org/10.11591/ijeecs.v30.i1.pp246-256
• Nivel de acceso: acceso abierto
• Materia: Classify; Discovering; Latent dirichlet allocation; Text corpus; Topics; https://purl.org/pe-repo/ocde/ford#2.02.04

This work aims at discovering topics in a text corpus and classifying the most relevant terms for each of the discovered topics. The process was performed in four steps: first, document extraction and data processing; second, labeling and training of the data; third, labeling of the unseen data; and fourth, evaluation of the model performance. For processing, a total of 10,322 "curriculum" documents related to data science were collected from the web during 2018-2022. The latent dirichlet allocation (LDA) model was used for the analysis and structure of the subjects. After processing, 12 themes were generated, which allowed ranking the most relevant terms to identify the skills of each of the candidates. This work concludes that candidates interested in data science must have skills in the following topics: first, they must be technical, they must have mastery of structured query language, mastery of programming languages such as R, Python, java, and data management, among other tools associated with the technology.