Técnicas big dataanálisis de textos a gran escala para la investigación científica y periodística

  1. Carlos Arcila-Calderón 1
  2. Eduar Barbosa-Caro 2
  3. Francisco Cabezuelo Lorenzo 3
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

  2. 2 Universidad del Norte
    info

    Universidad del Norte

    Barranquilla, Colombia

    ROR https://ror.org/031e6xm45

  3. 3 Universidad de Valladolid
    info

    Universidad de Valladolid

    Valladolid, España

    ROR https://ror.org/01fvbaw18

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Revista:
El profesional de la información

ISSN: 1386-6710,  1699-2407

Año de publicación: 2016

Título del ejemplar: Datos

Volumen: 25

Número: 4

Páginas: 623-631

Tipo: Artículo

DOI: 10.3145/EPI.2016.JUL.12 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: El profesional de la información

Resumen

Este trabajo conceptualiza el término big data y describe su importancia en el campo de la investigación científica en ciencias sociales y en las prácticas periodísticas. Se explican técnicas de análisis de datos textuales a gran escala como el análisis automatizado de contenidos, la minería de datos (data mining), el aprendizaje automatizado (machine learning), el modelamiento de temas (topic modeling) y el análisis de sentimientos (sentiment analysis), que pueden servir para la generación de conocimiento en ciencias sociales y de noticias en periodismo. Se expone cuál es la infraestructura necesaria para el análisis de big data a través del despliegue de centros de cómputo distribuido y se valora el uso de las principales herramientas para la obtención de información a través de software comerciales y de paquetes de programación como Python o R.

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Fuente de los datos: Dialnet