# Multidimensional Visualization of Sound-Sense Harmony for [PERSON]'s Sonnets Classification
## 1 Introduction
Another interesting paper [12] is cast in psychology and psycholinguistic research and associates a phonetic analysis using comparisons in cross-domain meaning properties. As far as valence is concerned, we have the following associations: consonants are classified into sonorants and obstruents and are associated with good/bad, respectively. Voiced speech sounds are perceived more positively than unvoiced speech sounds: unvoiced consonants, fricatives, and short vowels have been associated with negative valence; while voiced consonants and long vowels are associated with positive valence, voiced obstruents (stops, affricates, and fricatives) are associated with negative valence. More abstract properties are pointedness--which is associated with a threat--hardness, bigness, shape, weight, and mood. The positively correlated rounded/calming and pointed/exciting scales are reflected in common phonetic features: sonorants, voiced consonants, and back rounded vowels for rounded/calming on one side, and obstruents, unvoiced consonants, and front unrounded vowels for pointed/exciting; in the shape and weight domains, one scale (rounded or light) is associated with sonorants while the other (pointed or heavy) is associated with obstruents; hardness is indicated by obstruents and unvoiced consonants but not vowels, while softness is indicated by sonorants (and so unvoiced consonants are not relevant), and by back rounded vowels; bigness is associated with obstruents, voiced consonants, and back rounded vowels, while smallness is indicated only by stops and unvoiced consonants, not by af/fricatives or back vowels for the rounded and heavy scales, and front vowels for the pointed and light scales. Current NLP technologies are boasting their ability to tell the \"sentiment\" expressed by the opinions of people commenting on some public event. This is partly what we are aiming to achieve here, by computing--though with a different approach--the lexical or structural polarity contained in the words or phrases (more below). In addition to that computation, we measure the contribution of sounds, which may match or contrast the previous results. To produce a feasible comparison, the negative sentiment expressed by the words' sense with sad-sounding rhymes, poetic devices are matched as a whole, and the opposite for positive sentiment, and this is performed by scoring and computing the ratios for each class of sounds. Here, shown below is the way in which vowel and consonant sounds are organized:* Low, middle, high-front, and high-back. This is where the two classes low and middle are identified as promoting positive feelings, and the two high ones as inducing negative ones: the ratios are produced by dividing the number of low-middle by the number of high. As for the consonants, the sounds are organized into three main classes and two types:
* Obstruents (plosives, affricates), continuants (fricatives), sonorants (liquids, vibrants, approximants) plus the distinction into voiced vs. unvoiced. In the case of consonants, the ratios are computed by dividing the sum of continuants and sonorants by the number of obstruents; a second parameter is the ratio obtained by dividing the number of voiced by unvoiced. Whenever the value of the ratios is above 1, positive results are obtained; the contrary applies whenever values are below 1. In this way, counting results are immediate and very effective. The lexical and semantic approach to deriving the sentiment of each sonnet operates a first subdivision of harmonic and disharmonic sonnets into negatively vs. positively marked sonnets. Measuring relations reveals a constant contrasting attitude induced by the sound-sense agreement, which is interpreted as an underlying hidden intention to produce some form of ironic mood in [PERSON]'s sonnets. However, detecting irony vs. sarcasm and neutral mood requires a much deeper and accurate analysis of the semantics and the pragmatics of the sonnets.