Version 1
: Received: 2 October 2024 / Approved: 3 October 2024 / Online: 3 October 2024 (16:31:30 CEST)
How to cite:
Delmonte, R.; Busetto, N. Multidimensional Visualization of Sound-Sense Harmony for Shakespeare's Sonnets Classification. Preprints2024, 2024100286. https://doi.org/10.20944/preprints202410.0286.v1
Delmonte, R.; Busetto, N. Multidimensional Visualization of Sound-Sense Harmony for Shakespeare's Sonnets Classification. Preprints 2024, 2024100286. https://doi.org/10.20944/preprints202410.0286.v1
Delmonte, R.; Busetto, N. Multidimensional Visualization of Sound-Sense Harmony for Shakespeare's Sonnets Classification. Preprints2024, 2024100286. https://doi.org/10.20944/preprints202410.0286.v1
APA Style
Delmonte, R., & Busetto, N. (2024). Multidimensional Visualization of Sound-Sense Harmony for Shakespeare's Sonnets Classification. Preprints. https://doi.org/10.20944/preprints202410.0286.v1
Chicago/Turabian Style
Delmonte, R. and Nicolò Busetto. 2024 "Multidimensional Visualization of Sound-Sense Harmony for Shakespeare's Sonnets Classification" Preprints. https://doi.org/10.20944/preprints202410.0286.v1
Abstract
In this paper we focus on the association of sound and sense harmony in the collection of sonnets written by Shakespeare in the XVI° beginning of XVII° century and propose a new four-dimensional representation to visualize them by means of the system called SPARSAR. To compute the degree of harmony and disharmony, we have automatically extracted the sound grids of all the sonnets and have combined them with the semantics and polarity expressed by their contents. We explain in details the algorithm and show the representation of the whole collection of 154 sonnets and comment them extensively. In a second experiment, we use data from manual annotation of the sonnets for Satire detection using the Appraisal Theory Framework, to gauge the system's accuracy in matching these data with the output of the automatic algorithm for sound-sense harmony. The results obtained with an 94.6% accuracy confirm the obvious fact that the poet has to account for both sound and meaning in the choice of words.
Keywords
SPARSAR = Specialized NLP system for English Poetry; Automatic Analysis of XVIth Century English Poetry; Multidimensional Visualization of Linguistic and Poetic Content; Creating Graphic Interface with Coloured Boxes of Different Size; Computing Sound-Sense Harmony; Comparing Phonetic and Phonological Features with Meaning; Automatic Lexical and Semantic Sentiment Analysis of Poetry; Appraisal Theory Framework.
Subject
Arts and Humanities, Literature and Literary Theory
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.
