# Multidimensional Visualization of Sound-Sense Harmony for [PERSON]'s Sonnets Classification
## 5 Discussion  
The contribution of this article is to present a new paradigm by means of which to assess poetic content, that we called Sound-Sense Harmony. The algorithm that produces the evaluation of each poem is based on the output of a symbolic system for text analyses called _SPARSAR_ (but see Appendix A for more information), mainly used for automatic poetry reading, which has been enriched with the graphical output that shows a multidimensional representation of the contents of a poem or a collection of poems, thus allowing an immediate visual comparison. In addition, the article shows that deep semantic processing like the one contained in the ATF can be made to compare with poetic sound-based devices to produce a more comprehensive analysis. In sum, the article features two experiments: the first one directly uses the output of the ASSH to produce the multidimensional representation; the second one adds the information coming from the lexicon containing the result of the manual annotation of the sonnets with ATF, and features the use of the sound-sense harmony with the aim of telling apart Sarcasm from Irony and these two from Neutral.  
More importantly, the experiments documented in this article were all carried out with a symbolic system, _SPARSAR_, developed in the last 20 years or so and fully evaluated in a number of shared tasks. Deep Neural Networks and Large Language Models are unable to carry out complex tasks like the one proposed here: first of all, they cannot produce a correct and complete grapheme to phoneme (G2P) transcription of the text contained in the 154 sonnets, whereby the sound is derived to be subsequently matched with the semantic annotation. So-called G2P realized using various techniques, are all based on the only freely available dictionary made in CMU. However, their performance is highly error-prone with a WER documented in [32] averaging 20%. We tried to carry out the same with ChatGPT 4.0 using two sonnets and received the warning that the results were not satisfactory and that we had better carry out the transcription by ourselves using the CMU dictionary. In addition, there is no possibility for DNNs and LLMs to automatically annotate the sonnets using Appraisal Theory Framework (henceforth ATF) schemas. For these reasons, we stick to the symbolic system, but are unable to make comparisons with other systems for the difficulty of the task. In addition, to our knowledge, there is no previous attempt at combining phonetic information with ATF semantic classification, and in general with semantic classification, to produce a more comprehensive interpretation of the content of a poem. We also used the automatic lexically driven ATF classification to correct some slight inconsistencies resulting from the manual annotation, showing that using a simple matching algorithm at word and phrase levels on the basis of a well-wrought lexicon can produce improvements on manual annotation when the task is semantically very heavy and hard to apply consistently.  
Seeing that the system producing the graphical representations can work with any English poem, the corresponding application could be used for literature teaching programmes. It could also be used to analyze the sentiment expressed in short texts written daily by columnists, or opinions written in X or on FB.  
[PERSON]'s sonnets are semantically ambiguous and this ambiguity is used to produce Irony. SPARSAR is unable to compute non-literal language at present, even though it might be equipped with a specialized module to that aim. This would cover both ambiguous meaning and figures of speech like similes, metaphors, chiasmus, oxymorons, and other similar devices.