Multidimensional Visualization of Sound–Sense Harmony for Shakespeare’s Sonnets Classification

In this article, we focus on the association of sound and sense harmony in the collection of sonnets written by Shakespeare in the XVI° beginning of the 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 automatically extracted the sound grids of all the sonnets and combined them with the semantics and polarity expressed by their contents. We explain in detail the algorithm and show the representation of the whole collection of 154 sonnets and comment on them extensively. In a second experiment, we use data from the 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 a 94.6% accuracy confirm the obvious fact that the poet has to account for both sound and meaning in the choice of words.

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Publication Details

Journal
Applied Sciences
Published
2024-12-20
DOI
https://doi.org/10.3390/app142411949
Primary Topic
Music and Audio Processing
Type
article
Field-Weighted Citation Impact
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article

Multidimensional Visualization of Sound–Sense Harmony for Shakespeare’s Sonnets Classification

Rodolfo Delmonte, Nicolò Busetto
Applied Sciences
Music and Audio Processing
article

Multidimensional Visualization of Sound–Sense Harmony for Shakespeare’s Sonnets Classification

Rodolfo Delmonte, Nicolò Busetto
article en

Abstract

In this article, we focus on the association of sound and sense harmony in the collection of sonnets written by Shakespeare in the XVI° beginning of the 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 automatically extracted the sound grids of all the sonnets and combined them with the semantics and polarity expressed by their contents. We explain in detail the algorithm and show the representation of the whole collection of 154 sonnets and comment on them extensively. In a second experiment, we use data from the 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 a 94.6% accuracy confirm the obvious fact that the poet has to account for both sound and meaning in the choice of words.

Applied SciencesVol. 14(24)
Ca' Foscari University of Venice (IT), Menlo School (US)
Openalex Percentile: Top 74%
Music and Audio Processing
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