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.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:738074 |
| Date | January 2016 |
| Creators | Crosbie, Tess |
| Publisher | University of Bedfordshire |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/10547/622525 |
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