A computer assisted analysis of literary text: from feature analysis to judgements of literary merit

5.00
Hdl Handle:
http://hdl.handle.net/10547/622525
Title:
A computer assisted analysis of literary text: from feature analysis to judgements of literary merit
Authors:
Crosbie, Tess
Abstract:
Using some of the tools developed mainly for authorship authentication, this study develops a toolbox of techniques towards enabling computers to detect aesthetic qualities in literature. The literature review suggests that the style markers that indicate a particular author may be adapted to show literary style that constitutes a "good" book. An initial experiment was carried out to see to what extent the computer can identify specific literary features both before and after undergoing a "corruption" of text by translating and re-translating the texts. Preliminary results were encouraging, with up to 90 per cent of the literary features being identifi ed, suggesting that literary characteristics are robust and quanti fiable. An investigation is carried out into current and historic literary criticism to determine how the texts can be classified as "good literature". Focus groups, interviews and surveys are used to pinpoint the elements of literariness as experienced by human readers that identify a text as "good". Initially identified by human experts, these elements are confirmed by the reading public. Using Classics as a genre, 100 mainly fiction texts are taken from the Gutenberg Project and ranked according to download counts from the Gutenberg website, an indicator of literary merit (Ashok et al., 2013). The texts are equally divided into five grades: four according to the download rankings and one of non- fiction texts. From these, factor analysis and mean averages determine the metrics that determine the literary quality. The metrics are qualified by a model named CoBAALT (computer-based aesthetic analysis of literary texts). CoBAALT assesses texts by Jane Austen and D. H. Lawrence and determines the degree to which they conform to the metrics for literary quality; the results demonstrate conformity with peer reviewed literary criticism.
Affiliation:
University of Bedfordshire
Publisher:
University of Bedfordshire
Issue Date:
Nov-2016
URI:
http://hdl.handle.net/10547/622525
Type:
Thesis or dissertation
Language:
en
Description:
A thesis submitted to the University of Bedfordshire in ful lment of the requirements for the degree of Doctor of Philosophy
Appears in Collections:
PhD e-theses

Full metadata record

DC FieldValue Language
dc.contributor.authorCrosbie, Tessen
dc.date.accessioned2018-03-07T12:22:49Z-
dc.date.available2018-03-07T12:22:49Z-
dc.date.issued2016-11-
dc.identifier.urihttp://hdl.handle.net/10547/622525-
dc.descriptionA thesis submitted to the University of Bedfordshire in ful lment of the requirements for the degree of Doctor of Philosophyen
dc.description.abstractUsing some of the tools developed mainly for authorship authentication, this study develops a toolbox of techniques towards enabling computers to detect aesthetic qualities in literature. The literature review suggests that the style markers that indicate a particular author may be adapted to show literary style that constitutes a "good" book. An initial experiment was carried out to see to what extent the computer can identify specific literary features both before and after undergoing a "corruption" of text by translating and re-translating the texts. Preliminary results were encouraging, with up to 90 per cent of the literary features being identifi ed, suggesting that literary characteristics are robust and quanti fiable. An investigation is carried out into current and historic literary criticism to determine how the texts can be classified as "good literature". Focus groups, interviews and surveys are used to pinpoint the elements of literariness as experienced by human readers that identify a text as "good". Initially identified by human experts, these elements are confirmed by the reading public. Using Classics as a genre, 100 mainly fiction texts are taken from the Gutenberg Project and ranked according to download counts from the Gutenberg website, an indicator of literary merit (Ashok et al., 2013). The texts are equally divided into five grades: four according to the download rankings and one of non- fiction texts. From these, factor analysis and mean averages determine the metrics that determine the literary quality. The metrics are qualified by a model named CoBAALT (computer-based aesthetic analysis of literary texts). CoBAALT assesses texts by Jane Austen and D. H. Lawrence and determines the degree to which they conform to the metrics for literary quality; the results demonstrate conformity with peer reviewed literary criticism.en
dc.language.isoenen
dc.publisherUniversity of Bedfordshireen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectliterary analysisen
dc.subjectcomputer analysis of literatureen
dc.subjectdigital humanitiesen
dc.subjectEnglish literatureen
dc.titleA computer assisted analysis of literary text: from feature analysis to judgements of literary meriten
dc.typeThesis or dissertationen
dc.contributor.departmentUniversity of Bedfordshireen
dc.type.qualificationnamePhDen_GB
dc.type.qualificationlevelPhDen
dc.publisher.institutionUniversity of Bedfordshireen
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