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

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

Vílchez-Román, Carlos; Huamán-Delgado, Farita; Sanguinetti-Cordero, Sol;
URI: http://repositorio.ulima.edu.pe/handle/ulima/8253
Fecha: 2019

Resumen:

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.

Citación APA:

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

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Springer

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Pendiente / Pendiente

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Pendiente

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