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I Catching : Computationally Operationalising Narrative Perspective for Stylometric Analysis

Computationally Operationalising Narrative Perspective for Stylometric Analysis
Authors: Van Rossum; Van Zundert; Van Dalen-Oskam;

I Catching : Computationally Operationalising Narrative Perspective for Stylometric Analysis

Abstract

An automated way to measure a text's perspective is essential to a computational analysis of literary style and narrative. However, no such measure of narrative perspective exists to date. We aim to provide such a measure by taking two approaches to operationalise narrative perspective. The first measure is a machine learning approach, the second is based on computing the ratio of combinations of pronouns. Our research demonstrates that narrative perspective can be predicted with high accuracy and provides a reflection on the advantages and pitfalls of both methods.

Country
Netherlands
Keywords

computational, Narrative perspective, Operationalisation, perspective, narrative analysis, I-index, Style, Literariness, narratology, Digital humanities

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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