Network analysis of narrative content in large corpora

Saatviga Sudhahar; Gianluca De Fazio; Roberto Franzosi; Nello Cristianini

doi:10.1017/S1351324913000247

Network analysis of narrative content in large corpora

Saatviga Sudhahar, Gianluca De Fazio, Roberto Franzosi, Nello Cristianini

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Abstract

We present a methodology for the extraction of narrative information from a large corpus. The key idea is to transform the corpus into a network, formed by linking the key actors and objects of the narration, and then to analyse this network to extract information about their relations. By representing information into a single network it is possible to infer relations between these entities, including when they have never been mentioned together. We discuss various types of information that can be extracted by our method, various ways to validate the information extracted and two different application scenarios. Our methodology is very scalable, and addresses specific research needs in social sciences.

Original language	English
Pages (from-to)	81-112
Number of pages	32
Journal	Natural Language Engineering
Volume	21
Issue number	1
Early online date	11 Sept 2013
DOIs	https://doi.org/10.1017/S1351324913000247
Publication status	Published - 1 Jan 2015

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This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Cambridge University Press at https://www.cambridge.org/core/journals/natural-language-engineering/article/network-analysis-of-narrative-content-in-large-corpora/7B1FFB891E8B3751016B2AE46FCF76C1 . Please refer to any applicable terms of use of the publisher.
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AU - Franzosi, Roberto

AU - Cristianini, Nello

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AB - We present a methodology for the extraction of narrative information from a large corpus. The key idea is to transform the corpus into a network, formed by linking the key actors and objects of the narration, and then to analyse this network to extract information about their relations. By representing information into a single network it is possible to infer relations between these entities, including when they have never been mentioned together. We discuss various types of information that can be extracted by our method, various ways to validate the information extracted and two different application scenarios. Our methodology is very scalable, and addresses specific research needs in social sciences.

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Network analysis of narrative content in large corpora

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