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dc.contributor.authorVílchez Román, Carlos
dc.contributor.authorHuamán Delgado, Farita
dc.contributor.authorSanguinetti Cordero, Sol
dc.contributor.otherSanguinetti Cordero, Soles_PE
dc.date.accessioned2019-04-08T15:44:26Z
dc.date.available2019-04-08T15:44:26Z
dc.date.issued2019
dc.identifier.citationVílchez-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_21es_PE
dc.identifier.urihttps://hdl.handle.net/20.500.12724/8253
dc.descriptionIndexado en Scopuses_PE
dc.description.abstractMore 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.en_EN
dc.formatapplication/pdf
dc.language.isoenges_PE
dc.publisherSpringeres_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccess*
dc.sourceUniversidad de Limaes_PE
dc.sourceRepositorio Institucional - Ulimaes_PE
dc.subjectEmpresases
dc.subjectEnterprises
dc.subject.classificationCiencias empresariales y económicas / Administraciónes
dc.titleTopic modeling applied to business research: A latent dirichlet allocation (LDA)-based classification for organization studiesen
dc.typeinfo:eu-repo/semantics/conferenceObjectes_PE
dc.type.otherArtículo de conferencia en Scopuses_PE
dc.publisher.countryCHes_PE
dc.identifier.doihttps://doi.org/10.1007/978-3-030-11680-4_21


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