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.

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.