Text mining and big textual data: Relevant statistical models

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A general overview is provided through examples and case studies, retrieved from research experiences, to foster description and debate on effectiveness in Big Data environments. At issue are early stage case studies relating to: research publishing and research impact; literature, narrative and foundational emotional tracking; and social media, here Twitter, with a social science orientation. Central relevance and importance will be associated with the following aspects of analytical methodology: context, leading to availing of semantics; focus, motivating homology between fields of analytical orientation; resolution scale, which can incorporate a concept hierarchy and aggregation in general; and acknowledging all that is implied by this expression: correlation is not causation. Application areas are: quantitative and also qualitative assessment, narrative analysis and assessing impact, and baselining and contextualizing, statistically and in related aspects such as visualization.

Original languageEnglish
Title of host publicationNew Statistical Developments in Data Science - SIS 2017
EditorsAlessandra Petrucci, Filomena Racioppi, Rosanna Verde
PublisherSpringer New York LLC
Pages39-52
Number of pages14
Volume288
ISBN (Print)9783030211578
DOIs
Publication statusPublished - 21 Aug 2019
EventSIS Conference on Statistics and Data Science: New Challenges, New Generations - University of Florence, Florence, Italy
Duration: 28 Jun 201730 Jun 2017
http://meetings3.sis-statistica.org/index.php/sis2017/sis2017

Conference

ConferenceSIS Conference on Statistics and Data Science
CountryItaly
CityFlorence
Period28/06/1730/06/17
Internet address

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Murtagh, F. (2019). Text mining and big textual data: Relevant statistical models. In A. Petrucci, F. Racioppi, & R. Verde (Eds.), New Statistical Developments in Data Science - SIS 2017 (Vol. 288, pp. 39-52). Springer New York LLC. https://doi.org/10.1007/978-3-030-21158-5_4