Syria by Keith Douglas (a description of a strange country); 2) 1914 by Wilfred Owen (the description how the war may influence the human life); 3) Thrushes by ...
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Phonosemantic investigation of English and German poems Hanna Gnatchuk Abstract: The connection between the frequencies of the sounds and the emotional “mood” of the texts has been a matter of concern for poets, writers and linguists. In this research, we are aimed at revealing the linkage between the frequencies of English and German consonants and the “emotional” mood of poems. In order to achieve the given aim, we have analyzed 40 English and 40 German poems by different authors. The data were processed with the help of chi-square test and Chuprov’s coefficient K. As a result, we have found that there is a correspondence between the frequencies (or occurrences) of certain English and German sounds and the emotional “mood” of the poems. The results of the research may be of great use in composing the poems with a special emphasis on the creation of the emotional “mood” of the text.
Key words: sound symbolism, phonosemantics, quantitative methods. Introduction The text consists of a hierarchy of units, their properties, secondary units of different levels and their properties, and the interrelations between all of them. Grammatical rules are merely surface phenomena. At its creation, text is the product of the dynamic system called “language” and bears many traces of its birth. After having completed the (written/spoken) text, it becomes a static system but one can find in it the empirical realization of laws that cooperated at its birth. Needless to say, everything in texts happens by following consciously some rules, but while rules are ephemeral entities steadily changing and differing from language to language, laws are entities controlling the self-regulation and holding true for all languages. Rules cannot be derived, merely stated; laws must be derived from the background knowledge. Rules are empirical generalizations, laws are theoretical entities which must be derived and well corroborated in many languages. Laws hold true only if some boundary conditions (e.g. rules, rhythmic prescriptions) are fulfilled. Laws are statements about the behaviour of entities, their properties and their interrelations both in texts and in the physical reality. In order to corroborate the existence of laws after having derived them, one must operationalize and quantify the given properties for which they hold. Qualities are not always sufficient. Qualities are used to classify the entities but if in the linear sequence which is represented by a text some hypotheses should be tested, one needs some numbers, even if they are quite elementary, e.g. the transition frequencies between different classes of entities or the distances between equal entities or the frequencies of some entities. Exact research calls for quantification. Usually one says that qualities are primary and quantities secondary, but both are merely our concepts arising in the process of our thinking and linguistic abilities. In the evolution of language, qualities were captured conceptually earlier, quantities later on. Qualities are necessary for surviving (also for communication), quantities are necessary also for science. Now, if one needs quantities, one may obtain them by ascribing numbers to different degrees of a property. This is always possible. At the very beginning of structural linguistics, one simplified this operation by reducing any scale to a dichotomous one: an object possessed the given property or not, i.e. it belonged to a class or not. Problems arose when entities were found which did not want to belong to any of the polar classes, e.g. semivowels or glides. One “reclassified” and found a new dichotomous order. But one believed that the world is organized dichotomously – not only in the philosophy of some schools. Today we know that there is nothing of this kind in language or text, that there are no “given” data but we construct them in order to test hypotheses. One can begin to analyze wherever one wants. If one analyzes meanings, then Osgood’s (1957) semantic differential is a relatively objective scaling method. Semantic scaling should be
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performed merely with the aid of native speakers but if we analyze e.g. Latin, the problem will be insoluble and will bring rather variegated results. In grammar, there are a number of possibilities because grammar is the best scrutinized part of language. One of the possibilities would be the ascription of degrees to words according to the number of categories for which they may be shaped. For example (in German): Particle/preposition: no form building and no categories; or class ascription; Noun: form building in 2 categories (case, number), in some languages also gender or class; Pronoun/article: form building in 3 categories (gender, case, number) Adjectives: form building in 4 categories (gender, case, number, comparison); Verbs: form building in 5 categories (person, number, genus verbi, time, mode). In different languages it may be quite different, hence either one elaborates a general scaling taking into account “all” languages, or one adheres to a given language. Another view or an additive aspect may be the scaling of fusionality: Marking a category with several words, even if they are inflected (analysis). Marking a category on the given word (week inflection and synthesis). Marking a category in the given word (strong inflection and synthesis). Another possibility is the scaling of words according to the level of determination/ specification/predication. Thus noun has degree 1; verb and adjective have degree 2 because they are determiners of nouns, the same holds true for articles; adverb has degree 3 because it determines adjectives or verbs; prepositions have degree 4 because they determine nominal phrases, the same holds for conjunctions, etc. For individual languages this is different, one must find a unique, general scaling applicable to all languages. The problem is always the way of writing (together or separately), placing (left or right) of the determiner, etc. Replacing all respective entities by their degrees one obtains the specialized vector of the given text. This procedure can be performed with any property, e.g. lengths, complexities, coherences, polysemies, distances, etc. Now, the vector represents, so to say, the property space of the text. It is open for hypothesis statements, comparisons, text characterizations, language characterizations, even text sort characterizations, further the vector yields a time series with all of its properties, the numbers of values represent probability distributions which can be modelled, etc., and all this may be used to set up a control cycle whose edges may represent text or language laws if they were derived and sufficiently tested. If one rewrites the text qualitatively in form of symbols, e.g. N = noun, V = verb, … one obtains a sequence in which the position of an entity may serve as degree. The text develops is some way which can be captured by a mathematical function. One should avoid polynomials, instead one should try to find a substantiation for such a course. A still more complex way of characterization can be gone by considering the distances between identical entities (or elements of the same class) and set up the distribution of the distance. The properties of the distribution furnish us some knowledge of the association of entities, of their importance (centrality). The basic question that may be asked is: why do we want to measure everything in text? The answer is easy: any evolution in science is based on exact expression of properties and finding their links to other ones. If we study merely the paradigmatic and the syntagmatic rules, we stay at the lowest level. This is, nevertheless, necessary for learning the language. But even if we express the rules with, graphs, algebraic means and use complex mathematics, we stay at the surface. Any step into the depth necessitates quantification, measurement, setting up of hypotheses, testing them statistically, deriving them from background knowledge and incorporate them into a system of hypotheses representing a theory – even if it is embryonal.
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The phonosemantic analysis of English poems.
The organization of the sounds in the poems has always been a matter of concern for linguists and literature experts. In particular, the phonosemantic analyses of the poems have been carried out by Fonagy (1963), Zhuravlov (1974), Kushneryk (2004). I. Fonagy (1963) did statistical analyses of the poems written by Hugo, Verlaine, Petöfi and Rückert. He came to the conclusion that the frequencies of the sounds in the French, Hungarian and German languages are distributed in the following way: “hard” consonants prevail in the aggressive poems whereas “mild” consonants in the lyrical ones. Zhuravlov (1974) analyzed the Russian poems by Pushkin, Lermontov, Nekrasov, Block, Mayakovski and Esenin. He created a special method of automatic analysis. This analysis was aimed at revealing the frequencies of the sounds in the text that deviate from the average ones. Similar to Zhuravlov, Stern created the method for detecting the effect of phonosymbolism in poetry which was based on the values of the deviation of the frequencies of the sounds from their normal speech frequencies. Kushneryk (2004) has analyzed Germam, Ukrainian and Russian poems using the chi-square test. The linguist has revealed that there is a dominance of certain phonemes in the poems with certain emotional tints. Nevertheless, the previous investigations show that the phonosemantic analyses of the poems using statistical methods remain quite effective in order to receive more authentic results. The methodology and the results of the research The aim of our research is to confirm or refute the correlation between the frequencies of the English consonants and the “mood” of the poems. The data for analysis. We have analyzed the frequencies of consonants in 40 English poems. These poems were divided into two groups: a) the poems with a “positive” mood (the poetic pieces aimed at evoking pleasant feelings (such as love, happiness or gaiety) in the reader or describing pleasant events; b) the poems with a “negative” mood (the poetic pieces that arouse unpleasant feelings such as anger, resentment, horror or fear). Furthermore, we took into account the important demands for any linguo-statistical research: the homogeneity in chronology, theme and genre. In particular, we have analyzed the poems of the first half of the twentieth century (the homogeneous demand for chronology). The poems were divided into two groups: the poems with positive and negative mood (the demand for theme). Finally, our analysis was done within the genre of poetry (the homogeneous demand for genre). In such a way, the poems with the “positive mood” are as follows: 1) A rose is a rose by John Freeman (a beautiful description of rose and love); 2) Daisy by Richard Eldington (a romantic recollection of the author’s first love); 3) In the heart of contemplation by C. Day Lewis (a bright description of the nature and the character’s optimistic thoughts); 4) Daybreak by Stephen Spender (a romantic description of the woman’s awakening); 5) In May by William Henry Davies (the description of the author’s dream); 6) Suburban Dream by Edwin Muir (an impressive description of the author’s walking); 7) Lullaby by Yeats (a soothing description of the beauties of sleep); 8) Nightingales by Robert Bridges (a “melodious” description of nature); 9) Charms by William Henry Davies (a touching description of a loving girl); 10) The Angel and the Girl by Edwin Muir (a positive description of the harmony between a girl and an angel); 11) Love Poem by Kathleen Raine (a fabulous description of the love emotions); 12) Beauty by Wilfred Owen (the ideas of the beauty in the life); 13) Most lovely shade by Edith Sitwell (a detailed description of the precious splendours); 14) Vilanella of Spring Bells by Keith Douglas (the description of the town’s spring and its splendid atmosphere); 15) Fern Hill by Dylan Thomas (the description of the author’s youth); 16) Their very memory by Edmund Blunden (a positive description of the author’s mood); 17) Tulips by Sylvia Plath (the author’s consideration of tulips); 18) The day that I have loved by Rupert Brooke (a magnificent recollection of the day); 19) The three cherry trees by Walter De la
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Mare (the description of a beautiful lady); 20) Hyacinth by Kathleen Raine (the description of the hyacinth as a symbol of peace). The group of poems with the “negative” mood includes the following poetic pieces: 1) Syria by Keith Douglas (a description of a strange country); 2) 1914 by Wilfred Owen (the description how the war may influence the human life); 3) Thrushes by Ted Hughes; 4) Among those killed in the dawn raid was a man aged a hundred by Dylan Thomas (the character’s death); 5) Assaulted of Angels by Michael Roberts (the fear of the angels’ attack); 6) A refusal to mourn the death by fire of a child in London by Dylan Thomas (the character’s attempts to survive); 7) In Conjunction by Charles Madge (a depressive description of the surroundings); 8) The Fury of Aerial Bombardment by Richard Eberhart (pessimistic considerations of life); 9) The Course of a Particular by Wallace Stevens (a shrilling cry); 10) A meeting with despair by Thomas Hardy (the character’s experience); 11) Orpheus in the Underworld by David Gascoyne (a miserable description of the character); 12) The turtle Dove by Geoffrey Hill (the heart’s sufferings); 13) A love story by Robert Graves (character’s unhappy love); 14) On a return from Egypt by Keith Douglas (the character’s desolate state); 15) September 1, 1939 by W. H. Auden (the author’s consideration of the past); 16) A dead warrior by Laurence Houseman (the soldier’s ash); 17) Dead Man’s Dump by Isaac Rosenberg (gloomy recollections); 18) Farewell Poem by Keith Douglas (the dissatisfaction of the human life); 19) The Widow by Walter De la Mare (the widow’s suffering); 20) Departure by Thomas Hardy (the description of the farewell emotions). In such a way, 24 English consonants were under analysis in the above-mentioned poems. At first, the occurrences of each sound were counted in each group of poems (positive and negative). The results are given in Table 1. Table 1 The occurrences of English consonants in the poems with positive and negative mood
[b] [d] [t] [k] [g] [m] [n] [ŋ] [p] [r] [s] [Ө] [ð] [v] [w] [z] [d͡ʒ] [f] [t͡ʃ] [l] [h] [j] [ʃ] [ʒ]
Poems with positive mood 223 419 508 249 95 332 470 139 152 283 469 62 407 74 213 338 27 204 31 507 124 70 55 6
Poems with negative mood 162 408 612 301 93 376 498 134 136 332 478 65 332 105 197 331 49 216 44 506 136 44 72 4
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Table 1 shows how many times the consonant occurs in both positive and negative group of poems. Then the results (cf. Table 1) were treated with the help of the chi-square test and Chuprov’s coefficient. Chi-square test is a statistical method which is aimed at finding the correspondence between the occurrences (frequencies) of the consonants and the “mood” of the poems. The values of the chi-square are obtained according to the following formula: (𝑂−𝐸)2
(1) x2 = ∑
𝐸
Here O – actual numbers; E – theoretically expected numbers. Nevertheless, it will be relevant in statistical research to give the data in the form of alternative tables. For illustration, we give the example of Table 2 where the distribution of the occurrences (frequencies) of English consonant [b] in the poems with positive and negative “mood” is available: Table 2 The frequency distribution of the English consonant [b] in the poems with positive and negative “mood” Positive mood 223 (a) 5234 (c) 5457
[b] Others Total
Negative mood 162 (b) 5469 (d) 5631
Total 385 10703 11088 = N
In this case it is more convenient to use the following formula of the chi-square: (2)
a, b, c, d – the empirical values in the alternative table N – the total amount of observations.
According to Levitskij (1998) the chi-square test helps to confirm “zero” hypothesis (namely, to confirm that there is no connection between the objects under analysis), or refute it (to admit the existence of this connection). Zero hypothesis is refuted only in that case when the value of X2 exceeds 3.841 (with df = 1). In such a way, the value of the English consonant [b] is X2 = 11.4 (Table 3) which shows that there is the connection between the frequencies of this consonant and the group of poems with a positive “mood”. The results of the chi-square test for English consonants are given in Table 3. It is better to remark here that the chi-square determines the presence or the absence of the connection between two analyzed features. In order to reveal the degree of that connection, we use Chuprov’s coefficient (coefficient of contingency K). The value of coefficient K is to be found according to the following formula: 𝑥2
(3) K = √
𝑁 √(2−1)(2−1)
𝑥2
= √𝑁
K – coefficient of contingency N – the total amount of observations X2 – the value of chi-square Since the distribution of Chuprov’s coefficient is not known, its value is merely a relativization of the chi-square. Table 3
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The values of the chi-square and coefficient K for English consonants in the poems with positive and negative “mood”
[b] [d]
Positive poems X2 = 11.4 K = 0.031 X2 = 5.2 K = 0.021
X2 = 8.4 K = 0.027 X2 = 4.1 K = 0.019
[t] [k] [g]
X2 = 0.09 K = 0.002 X2 = 2.0 K = 0.013 X2 = 3.5 K = 0.017
[m] [n] [ŋ] [p]
X2 = 3.0 K = 0.016 X2 = 1.3 K = 0.010 X2 = 3.1 K = 0.016 X2 = 0.001 K = 0.00
[r] [s] [ð]
X2 = 10.0 K = 0.029 X2 = 4.8 K = 0.020
[v] [w] [z] [d͡ʒ]
X2 = 1.6 K = 0.011 X2 = 6.2 K = 0.023 X2 = 6.0 K = 0.023 x2 = 1.4 K = 0.011 x2 = 1.9 K = 0.013
[f] [ʃ] [j] [ʒ]
x2 = 6.6 K = 0.024 x2 = 0.4 K = 0.005 x2 = 0.3 K = 0.005
[h] [l] [Ө] [ʧ]
Negative poems
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Table 3 contains the results of X2 and coefficient K for English consonants. The marked values show that there is a certain correlation between the usage of sounds and a certain group of poems. In such a way, there is a certain linkage between the English consonants [b] (x 2=11.4; K=0.031), [d] (x2=5.2; K=0.021), [ð] (x2=10.0; K=0.029), [z] (x2=6.2; K=0.023), [j] (x2=6.6; K=0.024) and “positive” group of poems, on the one hand, and on the other hand between the consonant [t] (x2=8.4; K=0.027), [k] (x2=4.1; K=0.019); [v] (x2=4.8; K=0.020), [ʤ] (x2=6.0; K=0.023) and “negative” group of poetic pieces. It is worth mentioning here that the results in Gnatchuk (2014) have shown that the consonants [b], [d], [ð], [j], [z] were evaluated by English native speakers as smooth, kind and pleasant. Moreover, all these five consonants are voiced. The voiceless consonants [t], [k] were evaluated by the informants as strong, fast, rough and cruel whereas [v] and [d͡ʒ] – as kind, slow, small and strong. In such a way, the phonosemantic analysis of English poems has shown that there is a direct linkage between the frequencies of certain sounds and the mood of the poems. The next task of our analysis is to determine which English consonants have the highest symbolic potential and which group of poems has the highest symbolic activity. According to V. Levitskij, symbolic potential is “the ability of the sound to symbolize a certain notion”, whereas symbolic activity of the scales – “the ability of the notions or a group of notions to be symbolized by a certain sound” (Levitskij, 1998 :39). Judging from the values in Table 3, it is possible to state that such consonants as [b] (x2=11.4; K=0.031), [ð] (x2=10.0; K=0.029), [t] (x2=8.4; K=0.027), [j] (x2=6.6; K=0.024), [z] (x2=6.2; K=0.023), [d͡ʒ] (x2=6.0; K=0.023), [d] (x2=5.2; K=0.021); [v] (x2=4.8: K=0.020); [k] (x2=4.1; K=0.019) have the highest symbolic potential. In order to find the highest symbolic activity for the group of poems, it is necessary to add all the values for each consonant within positive and negative group of poems. In such a way, the group of poems with the positive mood proved to have a higher symbolic activity than the poems with the negative mood. The results are illustrated in Table 4. Table 4 The total values of x2 and coefficient K for British and American poems A group of poems with positive mood
A group of poems with negative mood
x2=45.79
x2=35.51
K=0.172
K=0.164
Therefore, it is possible to state from the above-mentioned analysis that a)
b) c)
2.
the consonants [b], [ð], [z], [j], [d] are characteristic of the highest frequency in the group of poems with positive mood according to the values of chi-square and coefficient K; the consonants [t], [k], [v], [d͡ʒ] proved to have the highest frequency in the group of poems with negative mood; the group of poems with positive mood proved to be the most semantically active.
Phonosemantic analysis of German and Austrian poems
Similar to the above-mentioned procedures and methods which were used for British poems, we have conducted the phonosemantic analysis for German and Austrian poems. In such a way, the following German poems with positive mood were under analysis: 1) Der reiche Sommer by Theodor Kramer (the description of a pleasant perception of the surrounding nature); 2) Es ist schön…by Theodor Kramer (pleasant emotions evoked by a loving woman); 3) Die
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schöne Stadt by Georg Trakl (the city’s life); 4) Die Luft ist warm by Georg Heim (a soothing description of the surroundings); 5) Frühlingsmittag by Hermann Hesse (a picture of spring’s nature full of flowers and music); 6) Viva! by Else Lasker-Schüler (the author’s wishes, intentions); 7) Zum neuen Jahr by Theodor Kramer (the preparation for New Year’s Eve); 8) Traubenjahr by Theodor Kramer (fruit and surrounding nature); 9) Helle Zeit by Theodor Kramer (a lively description of the working process); 10) August by Theodor Kramer (a vivid description of plenty of fruit); 11) Andacht by Ernst Goll (moving emotions towards the character); 12) Bedankt sei Mutter, dass du mich geboren by Theodor Kramer (gratitude towards everybody and everything for what they have done); 13) Der Wald by Georg Heim (the description of the forest’s life); 14) Der Liebende by Hermann Hesse (the emotions evoked by a lover); 15) Zum Geburtstag by Erich Mühsam (a positive description of a new federation); 16) Fröhliche Ostern by Kurt Tucholsky (lovely Easter celebration); 17) Im Ecker einsam saß ich by Anton Renk (Christmas night); 18) Ein froh und tröstlich Lied by Walter Flex (a positive influence of God’s “wonder-dress” on people); 19) Spät über den Häusern by Ernst Wilhelm Lotz (a metaphorical description of wonderful surroundings); 20) Frühlingsatem by Ernst Wilhelm Lotz (a happy description of the character’s gaiety). The next group of poems with the negative mood includes the following pieces: 1) Friedhof der Namenlosen by Theodor Kramer (a morose description of the atmosphere in the cemetery); 2) Die Narbe by Theodor Kramer (a terrible description of the author’s scar); 3) Das Gewitter by Georg Heym (the description of the nature when the storm breaks); 4) Frühjahr by Georg Heym (a desolate description of the emptiness and darkness of the surroundings); 5) Der Krieg by Georg Heym (the description of a fighting atmosphere); 6) Es wird nicht hell by Georg Heym (a pessimistic description of the nature and some events); 7) Hungersnot by Erich Mühsam (a cruel description of the hunger); 8) Vor einem Gewitter by Georg Heym (a “personified” description of death); 9) Toter Vater by Theodor Kramer (the author’s pity for his dead father); 10) Vorgezeichnet by Theodor Kramer (pessimistic surroundings); 11) Eiszeit der Herzen by Theodor Kramer (dark and “bitter” ideas); 12) Der Tod der Liebenden by Georg Heym (a terrible description of death); 13) Das Herz by Georg Trakl (morose events occurring in the evening); 14) Scheidender Schnitter by Theodor Kramer (a desperate description of the man’s way); 15) Die Hölle III by Georg Heym (the author’s desolate dreams); 16) Schwer ist die Ohnmacht zu ertragen by Theodor Kramer (the character’s fatigue and weakness); 17) Wüstes Schimpfen eines Wirtes by Alfred Lichtenstein (the owner’s irritation at the absence of people in his tavern); 18) Zernagung by Theodor Kramer (the author’s pessimistic recollection of a beaten man); 19) Erschöpfung by Theodor Kramer (the character’s miserable situation and his desolate ideas); 20) Vom Staub nur…by Theodor Kramer (the author’s recollections). Similar to the above-mentioned procedure with English consonants, we have counted the frequencies of each German sound in the poems with positive and negative mood. The results illustrated in Table 5. Table 5 The occurrences of German consonants in the poems with positive and negative mood
[b] [p] [t] [d] [k] [g] [m] [n] [ŋ]
Poems with positive mood 245 31 754 421 147 227 293 906 87
Poems with negative mood 205 34 723 384 164 226 288 831 77
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[f] [v] [s] [z] [ʃ] [ҫ] [x] [h] [j] [l] [ʧ] [ʤ] [pf] [r] [ts] [w]
178 68 322 220 200 144 76 101 22 449 1 0 19 460 72 236
147 74 286 128 261 168 94 116 12 389 0 0 18 468 111 201
Table 5 shows the frequencies of German consonants in the poems with positive and negative mood. These results were processed with the help of the chi-square (Formula 2) and coefficient K (Formula 3). The results are given in Table 6. Table 6 The values of chi-square and coefficient of contingency K
[b]
Optimistic poems X2 = 2.3 K = 0.014
X2 = 0.2 K = 0.004 X2 = 0.01 K = 0.000
[p] [t] [d]
X2 = 1.0 K = 0.009 X2 = 1.7 K = 0.012
[k] [g] [m]
-
[n]
X2 = 1.3 K = 0.010 X2 = 0.3 K = 0.005 X2 = 0.8 K = 0.008
[ŋ] [f]
X2 = 1.2 K = 0.010
X2 = 0.5 K = 0.006
[v] [s] [z] [ҫ]
Pessimistic poems
X2 = 1.1 K = 0.009 X2 = 10.5 K = 0.030 X2 = 2.9
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K = 0.016 X2 = 4.9 K = 0.021 X2 = 11.0 K = 0.031 X2 = 1.7 K = 0.012
[x] [ʃ] [h] X2 = 2.8 K = 0.015 X2 = 1.2 K = 0.010
[j] [l] [ʧ] [ʤ] [pf] [r]
X2 = 0.09 K = 0.002 X2 = 10.0 K = 0.030
[ts] [w]
X2 =1.7 K = 0.012
Table 6 presents the values of chi-square and coefficient K for German consonants. The marked values show that there is a certain linkage between the consonants [z] (x2 = 10.5; K = 0.030) with the poems with the positive mood and between the consonants [ts] (x2 = 10.0; K = 0.030), [ʃ] (x2 = 11.0; K = 0.031), [x] (x2 = 4.9; K = 0.021) with the poems with negative mood. According to the results of the psycholinguistic experiment (which are given in Gnatchuk 2014), the sound [ts] was evaluated by the majority of the respondents as a “strong”, “unpleasant”, “rough” and “cruel” sound; [x] – “weak”, “unpleasant”, “rough” and “cruel”; [ʃ] – strong, slow, big, pleasant. These three consonants which are statistically significant in the poems with the negative mood are voiceless. Nevertheless, the German consonant [z], which is statistically significant in the poems with positive mood, was evaluated by English native speakers as “strong”, “unpleasant”, “fast”, “rough” and “cruel”. The voiceless consonants [ts], [ʃ], [x] proved to dominate in the poems with negative mood whereas voiced sound [z] – in the poems with the positive mood. It shows that there is a correlation between the usage of certain German sounds and the mood of the poems. The next step of our investigation is to find the highest symbolic potential for German consonants. Therefore, the highest values of x2 and coefficient K are to be found for each consonant in both optimistic and pessimistic groups of poems in Table 6. In such a way, we have revealed that the highest symbolic potential is characteristic of the following sounds [ʃ] (x2 = 11.0; K = 0.031), [z] (x2 = 10.5; K = 0.030), [ts] (x2 = 10.0; K = 0.030), [x] (x2 = 4.9; K = 0.021). In order to find the highest symbolic activity of the groups of poems, we added the values of all consonants in the poems with positive and negative mood (Table 5). As a result, the group of poems with the negative mood proved to have a higher symbolic activity in comparison with the poems with “positive” mood. The results are given in Table 6: Table 7 2 The total values of x and coefficient K for German and Austrian poems The poems with positive mood x2 = 23.09 K = 0.124
The poems with negative mood x2 = 34.11 K = 0.142
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The above-mentioned analysis has helped us to do the following conclusions: a) the German consonant [z] turned out to possess the highest frequency in the poems with positive mood (one voiced sound); b) the German consonants [ts], [ʃ], [x] proved to have the highest frequency in the group of poems with negative mood (all the consonants are voiceless); c) the group of poems with negative mood turned out to be the most semantically active. The results show that it would be valuable to try to quantify also the text mood. Above, we made decisions merely on dichotomic judgements concerning text mood but did not ascribe the texts degrees on some scale. Needless to say, this can be done using Osgood’s semantic differential but this would presuppose the existence of team of collaborators. We must remove this task to the future.
References Fonagy, I. (1963). Die Metaphern in der Phonetik. – The Hague: Publisher. Gnatchuk, H. (2015). Phonosemantic features of English and German consonants. Glottometrics 30, 1-18, RAM-Verlag. Kushneryk, V. (2004). Fonosemantuzm y hermanskuh ta slovjanskuh movah [Phonosemantism in Germanic and Slavic Languages]. Chernivtsi: Ruta. Levitskij, V. (1998). Zvykovoj simvolizm: Osnovnje itogi [Sound symbolism: Basic results]. Chernivtsi: Ruta. Osgood, C.E., Suci, G., Tannenbaum, G.J. (1957). The measurement of meaning. Urbana: University of Illinoise Press. Shtern, A. S. (1967). Objektivnoje izychenije sybjektivnh otsenok zvukov rechi [Objective study of subjective grades for speech sounds]. Vopros porozhdenia rechi i obuchenia jazku, Moscow: Izd-vo Moscow University, 114-117. Zhuravlov, A. P. (1974). Foneticheskoje znachenije [Phonetic meaning],.Leningrad: Izd-vo Leningrad University.
