I Catching: Computationally Operationalising Narrative Perspective for Stylometric Analysis

TY - CONF

T1 - I Catching

T2 - DH Benelux 2020

AU - Van Rossum, Lisanne

AU - van Zundert, Joris J.

AU - van Dalen-Oskam, K.H.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

M3 - Paper

Y2 - 3 June 2020 through 5 June 2020

ER -