Big Data are hard to capture, store, search, share, analyze, and visualize. Without any doubts, Big Data represent the new frontier of data analysis, although their manipulation is far to be realized by standard computing machines. In this paper, we present a strategy to process and extract knowledge from Facebook data, in order to address marketing actions of a pharmaceutical company. The case study relies on a large Italians sample, interested in wellness and health care. The results of the study are very sturdy and can be easily replicated in different contexts.

Liberati, C., Mariani, P. (2018). Big data meet pharmaceutical industry: An application on social media data. In F. Mola, C. Conversano, M. Vichi (a cura di), Classification, (Big) Data Analysis and Statistical Learning (pp. 23-30). Springer Berlin Heidelberg [10.1007/978-3-319-55708-3_3].

Big data meet pharmaceutical industry: An application on social media data

Liberati, C
;
Mariani, P
2018

Abstract

Big Data are hard to capture, store, search, share, analyze, and visualize. Without any doubts, Big Data represent the new frontier of data analysis, although their manipulation is far to be realized by standard computing machines. In this paper, we present a strategy to process and extract knowledge from Facebook data, in order to address marketing actions of a pharmaceutical company. The case study relies on a large Italians sample, interested in wellness and health care. The results of the study are very sturdy and can be easily replicated in different contexts.
Capitolo o saggio
Big data; Dimensions reduction; Healthcare sector; Knowledge extraction;
Big Data; Dimensions reduction; Knowledge extraction; Healthcare sector
English
Classification, (Big) Data Analysis and Statistical Learning
Mola, F; Conversano, C; Vichi, M
2018
9783540238096
0
Springer Berlin Heidelberg
23
30
Liberati, C., Mariani, P. (2018). Big data meet pharmaceutical industry: An application on social media data. In F. Mola, C. Conversano, M. Vichi (a cura di), Classification, (Big) Data Analysis and Statistical Learning (pp. 23-30). Springer Berlin Heidelberg [10.1007/978-3-319-55708-3_3].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/187201
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