The technological revolution of the last years allowed to process different kinds of data to study several real-world phenomena. Together with the traditional source of data, textual data became more and more critical in many research domains, proposing new challenges to scholars working with documents written in natural language. In this paper, we explain how to prepare a set of documents for quantitative analyses and compare the different approaches widely used to extract information automatically, discussing their advantages and disadvantages.

Unsupervised analytic strategies to explore large document collections

Michelangelo Misuraca
;
2020

Abstract

The technological revolution of the last years allowed to process different kinds of data to study several real-world phenomena. Together with the traditional source of data, textual data became more and more critical in many research domains, proposing new challenges to scholars working with documents written in natural language. In this paper, we explain how to prepare a set of documents for quantitative analyses and compare the different approaches widely used to extract information automatically, discussing their advantages and disadvantages.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/304839
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