Big data techniques: Large-scale text analysis for scientific and journalistic research

Authors

  • Carlos Arcila-Calderón El profesional de la información
  • Eduar Barbosa-Caro
  • Francisco Cabezuelo-Lorenzo

DOI:

https://doi.org/10.3145/epi.2016.jul.12

Keywords:

Data, Big data, Data mining, Machine learning, Topic modeling, Sentiment analysis.

Abstract

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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Published

2016-07-29

How to Cite

Arcila-Calderón, C., Barbosa-Caro, E., & Cabezuelo-Lorenzo, F. (2016). Big data techniques: Large-scale text analysis for scientific and journalistic research. Profesional De La información, 25(4), 623–631. https://doi.org/10.3145/epi.2016.jul.12

Issue

Section

Non research articles