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Topic modeling applied to business research: A latent dirichlet allocation (LDA)-based classification for organization studies

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Date
2019
Author(s)
Vílchez Román, Carlos
Huamán Delgado, Farita
Sanguinetti Cordero, Sol
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Abstract
More than 1.5 million academic documents are published each year, and this trend shows an incremental tendency for the following years. One of the main challenges for the academic community is how to organize this huge volume of documentation to have a sense of the knowledge frontier. In this study we applied Latent Dirichlet Allocation (LDA) techniques to identify primary topics in organization studies, and analyzed the relationships between academic impact and belonging to the topics detected by LDA.
URI
https://hdl.handle.net/20.500.12724/8253
DOI
https://doi.org/10.1007/978-3-030-11680-4_21
How to cite
Vílchez-Román, C., Huamán-Delgado, F., Sanguinetti-Cordero, S. (2019). Topic Modeling Applied to Business Research: A Latent Dirichlet Allocation (LDA)-Based Classification for Organization Studies. En: Lossio-Ventura J., Muñante D., Alatrista-Salas H. (eds) Information Management and Big Data. SIMBig 2018. Communications in Computer and Information Science, vol 898 (pp 212-219). Springer, Cham. https://doi.org/10.1007/978-3-030-11680-4_21
Publisher
Springer
Research area / line
Desarrollo empresarial / Estrategias y comportamiento empresarial
Category / Subcategory
Pendiente / Pendiente
Subject
Empresas
Enterprises
Note
Indexado en Scopus
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Contacto: repositorio@ulima.edu.pe

Todos los derechos reservados. Diseñado por Chimera Software