TY - JOUR
T1 - Topic modeling applied to business research: A latent dirichlet allocation (LDA)-based classification for organization studies
AU - Vílchez Román, Carlos
AU - Huamán Delgado, Farita
AU - Sanguinetti Cordero, Sol
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
UR - https://hdl.handle.net/20.500.12724/8253
U2 - https://doi.org/10.1007/978-3-030-11680-4_21
DO - https://doi.org/10.1007/978-3-030-11680-4_21
M3 - Article (Contribution to Journal)
JO - Default journal
JF - Default journal
ER -