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.
|Translated title of the contribution||Big data techniques: Large-scale text analysis for scientific and journalistic research|
|Number of pages||9|
|Journal||Profesional de la Informacion|
|State||Published - 2016|
All Science Journal Classification (ASJC) codes
- Library and Information Sciences