Version 1
: Received: 7 July 2023 / Approved: 17 July 2023 / Online: 18 July 2023 (12:24:24 CEST)
How to cite:
Delmonte, R. Computing the Sound-Sense Harmony - in the poetic works by William Shakespeare and Francis Webb. Preprints2023, 2023071233. https://doi.org/10.20944/preprints202307.1233.v1
Delmonte, R. Computing the Sound-Sense Harmony - in the poetic works by William Shakespeare and Francis Webb. Preprints 2023, 2023071233. https://doi.org/10.20944/preprints202307.1233.v1
Delmonte, R. Computing the Sound-Sense Harmony - in the poetic works by William Shakespeare and Francis Webb. Preprints2023, 2023071233. https://doi.org/10.20944/preprints202307.1233.v1
APA Style
Delmonte, R. (2023). <strong>Computing the Sound-Sense Harmony - in the poetic works by William Shakespeare and Francis Webb</strong>. Preprints. https://doi.org/10.20944/preprints202307.1233.v1
Chicago/Turabian Style
Delmonte, R. 2023 "<strong>Computing the Sound-Sense Harmony - in the poetic works by William Shakespeare and Francis Webb</strong>" Preprints. https://doi.org/10.20944/preprints202307.1233.v1
Abstract
We assume that poetic devices have an implicit goal: producing an overall sound scheme that will induce the reader to associate intended and expressed meaning to the sound of the poem. Sounds may be organized into categories and assigned presumed meaning as suggested by traditional literary studies. In my work, I have extracted automatically the sound grids of all the sonnets by William Shakespeare and have combined them with the themes expressed by their contents. In a first experiment I have computed lexically and semantically based sentiment analysis obtaining an 80% of agreement. In a second experiment sentiment analysis has been substituted by Appraisal Theory thus obtaining a more fine-grained interpretation which in some cases contradicts the first one. The computation for the second poet - regarded by many critics the best of last century - includes both vowels and consonants. In addition, it combines automatic semantically and lexically based sentiment analysis with sound grids. The results produce visual maps that clearly separate poems into three clusters: negative harmony, positive harmony and disharmony where the latter instantiates the need by the poet to encompass the opposites in a desperate attempt to reconcile them.
Keywords
SPARSAR = Specialized NLP system for English Poetry organized into ten feeding modules and over twenty dictionaries; Automatic Analysis of English Poems; Visualization of Linguistic and Poetic Content; Creating Clusters of Boxes of Different Dimension one for each poem according to linguistic content and positioning each box in a space; Computing Sound-Sense Harmony; Comparing Phonetic and Phonological Features with Meaning; Automatic Lexical and Semantic Sentiment Analysis of Poetry; Appraisal Theory Framework.
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
