Modeling of topics applied to the analysis of the paper of automatic learning in systemic revisions
The objective of the research was to analyze the role of machine data learning in systematic literature reviews. The Natural Language Processing technique called topic modeling was applied to a set of titles and abstracts collected from the Scopus database. Specifically, the Latent Dirichlet Assignm...
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| Format: | Online |
| Language: | spa |
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Universidad Pedagógica y Tecnológica de Colombia
2022
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| Online Access: | https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/15271 |
| _version_ | 1801706008961089536 |
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| author | Grisales-Aguirre, Andrés Mauricio Figueroa-Vallejo, Carlos Julio |
| author_facet | Grisales-Aguirre, Andrés Mauricio Figueroa-Vallejo, Carlos Julio |
| author_sort | Grisales-Aguirre, Andrés Mauricio |
| collection | OJS |
| description | The objective of the research was to analyze the role of machine data learning in systematic literature reviews. The Natural Language Processing technique called topic modeling was applied to a set of titles and abstracts collected from the Scopus database. Specifically, the Latent Dirichlet Assignment (LDA) technique was used, from which it was possible to discover and understand the underlying themes in the collection of documents. The results showed the usefulness of the technique used in the exploratory literature review, by allowing the results to be grouped by theme. Likewise, it was possible to identify the specific areas and activities where machine learning has been applied the most, in relation to literature reviews. It is concluded that the LDA technique is an easy-to-use strategy and whose results allow a wide collection of documents to be approached in a systematic and coherent manner, notably reducing the review time. |
| format | Online |
| id | oai:oai.revistas.uptc.edu.co:article-15271 |
| institution | Revista de Investigación, Desarrollo e Innovación (RIDI) |
| language | spa |
| publishDate | 2022 |
| publisher | Universidad Pedagógica y Tecnológica de Colombia |
| record_format | ojs |
| spelling | oai:oai.revistas.uptc.edu.co:article-152712023-07-25T02:25:17Z Modeling of topics applied to the analysis of the paper of automatic learning in systemic revisions Modelado de tópicos aplicado al análisis del papel del aprendizaje automático en revisiones sistemáticas Grisales-Aguirre, Andrés Mauricio Figueroa-Vallejo, Carlos Julio topic modeling; machine learning; systematic reviews; Latent Dirichlet Allocation modelado de tópicos; aprendizaje automático; revisiones sistemáticas; Asignación Latente de Dirichlet The objective of the research was to analyze the role of machine data learning in systematic literature reviews. The Natural Language Processing technique called topic modeling was applied to a set of titles and abstracts collected from the Scopus database. Specifically, the Latent Dirichlet Assignment (LDA) technique was used, from which it was possible to discover and understand the underlying themes in the collection of documents. The results showed the usefulness of the technique used in the exploratory literature review, by allowing the results to be grouped by theme. Likewise, it was possible to identify the specific areas and activities where machine learning has been applied the most, in relation to literature reviews. It is concluded that the LDA technique is an easy-to-use strategy and whose results allow a wide collection of documents to be approached in a systematic and coherent manner, notably reducing the review time. El objetivo de la investigación fue analizar el papel del aprendizaje automático de datos en las revisiones sistemáticas de literatura. Se aplicó la técnica de Procesamiento de Lenguaje Natural denominada modelado de tópicos, a un conjunto de títulos y resúmenes recopilados de la base de datos Scopus. Especificamente se utilizó la técnica de Asignación Latente de Dirichlet (LDA), a partir de la cual se lograron descubrir y comprender las temáticas subyacentes en la colección de documentos. Los resultados mostraron la utilidad de la técnica utilizada en la revisión exploratoria de literatura, al permitir agrupar los resultados por temáticas. Igualmente, se pudo identificar las áreas y actividades específicas donde más se ha aplicado el aprendizaje automático, en lo referente a revisiones de literatura. Se concluye que la técnica LDA es una estrategia fácil de utilizar y cuyos resultados permiten abordar una amplia colección de documentos de manera sistemática y coherente, reduciendo notablemente el tiempo de la revisión. Universidad Pedagógica y Tecnológica de Colombia 2022-08-15 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion application/pdf text/xml https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/15271 10.19053/20278306.v12.n2.2022.15271 Revista de Investigación, Desarrollo e Innovación; Vol. 12 No. 2 (2022): Julio-Diciembre; 279-292 Revista de Investigación, Desarrollo e Innovación; Vol. 12 Núm. 2 (2022): Julio-Diciembre; 279-292 2389-9417 2027-8306 spa https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/15271/12481 https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/15271/13214 Derechos de autor 2022 Revista de Investigación, Desarrollo e Innovación |
| spellingShingle | topic modeling; machine learning; systematic reviews; Latent Dirichlet Allocation modelado de tópicos; aprendizaje automático; revisiones sistemáticas; Asignación Latente de Dirichlet Grisales-Aguirre, Andrés Mauricio Figueroa-Vallejo, Carlos Julio Modeling of topics applied to the analysis of the paper of automatic learning in systemic revisions |
| title | Modeling of topics applied to the analysis of the paper of automatic learning in systemic revisions |
| title_alt | Modelado de tópicos aplicado al análisis del papel del aprendizaje automático en revisiones sistemáticas |
| title_full | Modeling of topics applied to the analysis of the paper of automatic learning in systemic revisions |
| title_fullStr | Modeling of topics applied to the analysis of the paper of automatic learning in systemic revisions |
| title_full_unstemmed | Modeling of topics applied to the analysis of the paper of automatic learning in systemic revisions |
| title_short | Modeling of topics applied to the analysis of the paper of automatic learning in systemic revisions |
| title_sort | modeling of topics applied to the analysis of the paper of automatic learning in systemic revisions |
| topic | topic modeling; machine learning; systematic reviews; Latent Dirichlet Allocation modelado de tópicos; aprendizaje automático; revisiones sistemáticas; Asignación Latente de Dirichlet |
| topic_facet | topic modeling; machine learning; systematic reviews; Latent Dirichlet Allocation modelado de tópicos; aprendizaje automático; revisiones sistemáticas; Asignación Latente de Dirichlet |
| url | https://revistas.uptc.edu.co/index.php/investigacion_duitama/article/view/15271 |
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