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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Revue:
El profesional de la información

ISSN: 1386-6710,  1699-2407

Année de publication: 2016

Titre de la publication: Datos

Volumen: 25

Número: 4

Pages: 623-631

Type: Article

DOI: 10.3145/EPI.2016.JUL.12 DIALNET GOOGLE SCHOLAR lock_openAccès ouvert editor

D'autres publications dans: El profesional de la información

Objectifs de Développement Durable

Résumé

This paper conceptualizes the term big data and describes its relevance in social research and journalistic practices. We explain large-scale text analysis techniques such as automated content analysis, data mining, machine learning, topic modeling, and sentiment analysis, which may help scientific discovery in social sciences and news production in journalism. We explain the required e-infrastructure for big data analysis with the use of cloud computing and we asses the use of the main packages and libraries for information retrieval and analysis in commercial software and programming languages such as Python or R.

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La source de données: Dialnet