The computer-assisted textual research reveals a set of quantitative features (absolute and relative frequency, quantity, rank) of the words which articulate the meaning of a fiction text. This meaning is presented as nonverbal behaviour in Ian McEwan's novels Sweet Tooth and Solar under the research framework of quantitative analysis, in particular, computational text analysis. The set of quantitative features in each text was complemented by qualitative parameters (types of nonverbal behaviour: paralanguage, moving; groups of meaning of nonverbal behaviour: descriptive, nondescriptive) contextual nonverbal behaviour through its relation to the categories of “coherence” and “character” in Ian McEwan' fiction. Nonverbal communication possesses a great potential in conveying the meaning of the message in human interaction. Real-life nonverbal communication is transferred by a writer to a fictional text as nonverbal behaviour playing a significant role in readers' understanding of the nonverbal behaviour of characters and thus providing the coherence of the fictional text. The two types of nonverbal behaviour components - paralanguage (voice qualities) and moving (movements of the body) - establish the core of nonverbal behaviour presentation being the most frequent types which are referred to in Ian paralanguage and moving are not homogenous in the researched text contexts conveying two groups of meaning of nonverbal behaviour - descriptive (interprets fiction characters' nonverbal behaviour) and nondescriptive (explains fiction text coherence). Voyant-Tools software is applied to extract quantitative data in text corpora.
Tools, textual analysis
Nonverbal behaviour, quantitative features, paralanguage, moving, textual coherence, Voyant
Modern linguistics is closely connected with IT technologies which stimulate quantitative textual
researches, in particular statistical (quantittive) text analysis. British fiction of the 21st century is a
substantial source to study nonverbal behaviour. It is overflowing
with nonverbal behaviour
components, especially paralanguage and moving. F. Poyatos [
2022 Copyright for this paper by its authors.
behaviour contributes to both the description of characters’ nonverbal behaviour and the coherence of
the fictional text. Presently, the research of nonverbal behaviour in contemporary British fiction goes
far beyond simple language processing: fast and accurate systematic computer calculations, as J.
Flanders and F. Yannidis argue, provide more sophisticated tools and more reliable results in
quantitative analysis and interpretation [
The objective of the research is to detect a set of quantitative features of nonverbal behaviour in two
novels Sweet Tooth and Solar by I. McEwan [
Fictional text as a novel is traditionally a form of literature that has responded to social and political movements, and literary fiction in a certain period has close ties with societies and culture. The late seventies outlined a period of political, social and cultural change that reveals some of the fundamental characteristics of contemporary Britain from the end of World War II up to the present day. Literary critics regard the fiction of I. McEwan as “most continued reflection on the form of the novel, and the inherited tradition of modern (especially Anglophone) fiction and criticism” [12].
The novels under examination, Sweet Tooth and Solar by I. McEwan, are modern classic ones. Sweet Tooth explores the relationship between artistic wholeness and government propaganda to reveal diverse acceptance of literature; the boundary between real life and fiction is depicted throughout [13]. Solar is a fictional text that draws heavily on natural science and modern history references. It is a satire about a Nobel-winning physicist whose broken personal life and devious ambition make him chase a solar-energy based solution for climate change. In 2010, Solar got the Bollinger Everyman Wodehouse Prize, a British literary award for comic writing.
Any description of nonverbal behaviour by a writer is ipso facto significant since “a creator of the fictional texts would not record constant background noise and redundant paralanguage of everyday life” [14]. Novels by I. McEwan are the relevant source of investigation of nonverbal behaviour.
Ideas associated with contemporary British fiction expose the variety of linguistic phenomena to be studied as a set of quantitative features, e.g., absolute and relative frequency, quantity, the rank of the words. One of the productive ways of fictional text analysis is its inquiry with the help of computer software. Computational tools are privileged in extracting necessary data from a vast amount of fictional text. They allow to investigate and interpret different aspects and features of fictional texts, including the aspects of meaning.
Computational approaches to understanding this phenomenon exemplify accurate lexical and semantic data. Just as researchers can implement computational linguistics in various fields and through a wide assortment of tools and procedures, the research fields include a diverse range of topics [15]. Among them, computational text analysis plays a critical role in enriching qualitative approaches with quantitative ones [16, 17].
Linguists define a style in terms of a domain of language use (e.g., what choices a particular author makes in a specific genre, or in a certain text). B. Bloch draws attention to the style of a text as to the message carried by the frequency distributions of its linguistic features [20]. Therefore, style consists of choices made from the repertoire of the language [18]. While linguistic features do not constitute a fictional text’s “meaning”, a quantitative account of linguistic features serves to ground a stylistic interpretation and to help to explain why certain groups of meaning or types of nonverbal behaviour are possible [19, 20].
The quantitative research of the words representing nonverbal behavior involves the application of the following methods: textual analysis (semantics), quantitative analysis (computational linguistic) and qualitative analysis (interpretation).
Textual analysis is a holistic, systematic approach to the study of a fictional text by dividing it into
parts (e. g. words) to analyze each of its elements in combination with other textual elements as a set of
linguistic means that convey the semantic unity of a fictional text. In the context of a fictional text, the
characteristics of different textual levels are interdependent and not isolated [
Quantitative analysis implies handling quantitative data in linguistic study [22-24]. In our research quantitative analysis is a method of observing a fictional text in terms of its quantitative features (word frequency, word quantity, and word ranks). The quantitative analysis results obtained in the process of word/word form extraction applying computer software are presented in tables and diagrams. Being designed for a wide range of applications and users, Voyant-Tools is used in the present study as a reading and visualization environment to extract the words and indexes of their frequency; to find the context for each word among single fictional text through network analysis/keyword/feature extraction. By applying advanced analytical techniques, such as Naïve Bayes, Support Vector Machines (SVM), and other deep learning algorithms, we can explore and discover implicit relationships within structured and unstructured data.
Qualitative analysis is the method of interpretation that means studying the contexts of fictional text to define “paralanguage” and “moving” types of nonverbal behaviour, to sort out descriptive and nondescriptive groups of the meaning of nonverbal behaviour. Interpretation involves making equations between linguistic forms and the meanings contracted by the function of these forms in a context of a fictional text. The method also aims to make sense of quantitative data in definite corpora in the context of a fictional text.
The procedure involves the following steps of analysis as corpus processing: 1. The fictional texts Sweet Tooth (Corpus 1) and Solar (Corpus 2) by I. McEwan are selected and made available for uploading in Voyant-Tools software in pdf format as text corpora. Digitalized data are inspected for errors.
2. Data aggregation means a reduction of dimensions of the data by aggregating individual text elements into broader categories. Here, all of the data are reduced to word absolute frequency in the textual corpus. The words said, v (320), went, n (144) in Corpus 1, and the words said, v (217), came, v (108) in Corpus 2 are defined as the most frequent words in corpus concerning nonverbal behaviour presentation (see Table 1).
3. Data query is the extraction of specific data from stored items. The most frequent words, mostly verbs, and their word forms form two types of nonverbal behaviour – paralanguage (say*v; tell*, v; talk*, v; speak*, v; sound*, v; sound*, n; voice*, n) and moving (go*, v; come*, v; walk*, v; follow*, v; turn*, v), and two groups of the meaning of nonverbal behaviour – descriptive, nondescriptive. The word with the mark «*» means the word and its forms
4. We did quantitative data analysis considering the quantity and absolute frequency of the words and their word forms and applying Voyant-Tools to extract the words in two text corpora. The total quantity of “paralanguage”, “moving”, “descriptive meaning”, “nondescriptive meaning” is counted and compared in Table 2 and Table 3.
5. Qualitative data analysis considers fictional text contexts extraction by Voyant-Tools to interpret the meaning and sort out the types of nonverbal behaviour (paralanguage, moving) and groups of the meaning of nonverbal behaviour (nondescriptive, descriptive).
6. The research material consists of two corpora: Corpus 1 (Sweet Tooth) and Corpus 2 (Solar) by I. McEwan. The summary of each corpus, automatically generated by Voyant-Tools, is shown in Table 1 that exhibits that the corpora have approximately the same quantity of words and unique word forms. The vocabulary density, readability index, and average quantity of words are higher in Corpus 2. The table also shows the first fifteen more frequent words in corpora. The words said, v; went, v; came, v are the most frequent and valid in the researched texts.
Fictional text is a complex unit, which implies the creative consciousness of an author, so each lexical component (word) in its way is chosen to represent a common single aspiration of the whole text. M. Short and G. Leech, P. Simpson, B. Bloch, and R. Barthes [25] argue that textual analysis is the “stylistic of choice” in revealing the meaning of the text, which is an objective phenomenon, subjective experience, and the intersubjective meaning. The text permanently impregnates potential meanings and a reader that perceives, identifies and actualizes them.
Thus, the quantitative analysis “requires a complete description”, which is “not a list of certain elements”, but “the identification of a system of functions” [26]. A full-fledged quantitative analysis is always a holistic one: it identifies not the building material but the constructive relationships of the whole as a “complexly constructed meaning” [27]. The quantitative analysis approach divides a complex unity (fictional text) into units as products of its analysis (words), which, unlike elements, do not lose the properties of the whole entity, but present in their simplest, original form those properties of the whole entity, for the sake of which the analysis is undertaken [28]. So, we argue that quantitative analysis identifies building material to explain the whole text’s structural and semantic relationships as complexly constructed meaning. One of the most productive ways of understanding how a text works, as S. Statham notes, is to challenge it or intervene in its stylistic makeup in some way [29].
The revealing of both different types of meaning and the whole meaning of a fictional text is possible due to interpretation: it is a sense-making process or revelation of meaning. This intellectual operation cannot be reduced to an explanation, which, answering the question “why?” turns it from the present into the past. Interpretation, on the other hand, is oriented toward the future since it always explicitly or implicitly answers the question “why?” (“what” is the significance of this fact for a reader) [30]. Interpretation is a subjective process, a kind of dialogue between two realities: subjective and aesthetic ones. The interpretation is a mental turn into an object of self-interest [31].
Consequently, the interpreter’s task of a fictional text is to comprehend its semantic content “better than the author”; to understand the individual significance of the fictional text as an aesthetic experience. The purpose of interpretation in quantitative analysis is not a reconstruction of the author’s intention but the construction of meaning [32, 33]. It is a creative process of interaction of the reader with the text and its internal dialogue with its personality. Hence, there are not and cannot be two identical readings of the same fictional text – the same reader doesn’t make an identical sense every time he rereads a fictional text, i. e., a reader may ignore or not actualize the components of nonverbal behaviour that are objectively present in the text. Quantitative analysis is precisely that field, which establishes a particular sector of the adequacy of reader co-creation; it identifies the boundaries beyond which there is an area of the reader’s destructive reception.
4.1.
Nonverbal communication, in general, is associated with human nonverbal behaviour, and it ranges from aspects of voice to gestures, movements, and interpersonal spatial positioning, accessed by the vision and other senses. M. Danesi defined nonverbal communication as a group of human attributes or actions in which words are not involved but which have a shared social meaning. The criteria on the components of nonverbal communication also varied; it encompassed all kinds of nonverbal elements ranging from bodily signals to architecture [34].
The meanings of nonverbal communication (nonverbal behaviour) usually permeate the fictional text perceived, identified and understood by a reader. For the purpose of quantitative analysis, the term “nonverbal communication” refers to the forms of nonverbal behaviour exhibited by characters. A writer addresses a fictional text to a reader not directly but through a kind of “inner vision”, “inner hearing”, and “empathy for the characters”. This kind of impact is organized by the semiotic activity of the author (a narrator, to be exact), using a definite set of lexical units or words. “A fictional text is a purely intentional object and is a product of the author’s conscious” [35].
X. Jiang proves the importance of nonverbal communication in fiction, especially paralanguage (voice qualities) and kinetics (body movements) and how it contributes to an effective relationship between the text, the writer and the reader [36]. Nonverbal communication in a fictional text is transferred from real-life nonverbal communication presented by the author through the words which describe the characters’ nonverbal behaviour and are the way to contribute to textual coherence. Such transferring accounts for how the fictional text interlocks with the semantic process, notably those of “moving” and “saying”, and how these processes influence characters. As part of fictional text, the words denoting nonverbal behaviour are under two types of meaning: descriptive (denotes nonverbal behaviour and contributes to the characters’ nonverbal behaviour in fictional text) and nondescriptive (denotes nonverbal behaviour and contributes to fictional text coherence). Thus, representation of nonverbal behaviour is an indication of certain stylistic characteristics more accentuated in some writers than in others, and therefore an important touchstone for the quantitative analysis of fictional texts supported by processing techniques of computational linguistics.
This section manifests a computer-assisted case study of the words representing nonverbal behaviour in a fictional text through some quantitative data which expose a set of quantitative features in text corpora. Thus, we identified 1965 words in proper contexts to show the difference between two groups of nonverbal behaviour (paralanguage, moving) and two types of meaning (descriptive, nondescriptive). We delivered and visualized the results of the research as follow: Figure 1, Figure 2 (the most frequent words in corpora and their relative frequencies); Table 2, Table 3, Table 4, Table 5 (absolute frequencies, contexts, quantity of the words); Figure 3, Figure 4, Figure 5, Figure 6 (absolute frequencies of the words); Table 6 (rank and absolute frequencies of the words).
The significance of nonverbal behaviour presentation is connected to the frequency of words denoting paralanguage and moving in text corpora: Corpus 1 (Figure 1) and Corpus 2 (Figure 2).
The diagram indicates relative frequencies of the most significant words of nonverbal behaviour presentation in Corpus 1 (said, v (320); say, v (100); told, v (132); went, v (144)) and their distribution among 10 segments of the corpus text. The most unstable word said, v is the most frequent one and has a higher frequency index in the 9th text segment. The rest of the words do not have such sharp fluctuations, and they are approximately the same relative frequencies throughout the text.
The diagram conveys relative frequencies of the most significant words for nonverbal behaviour presentation in Corpus 2 (come, v (220); said, v (217); went, v (84); going, v (64)) and their distribution among 10 segments of the corpus text. The most unstable word said, v is the most frequent and has higher frequency usage in the 10th text segment. Compared to Corpus 1 the relative frequency of the word said, v falls in the 5th segment and rises sharply in the 10th segment of the textual corpus. The relative frequencies of the rest of the words, except going, v which increases in the 2nd and 8th segments, do not have such sharp fluctuations; they have approximately the same relative frequencies throughout the text corpus. The word said, v has a higher relative frequency in Corpus 1 – 0.0005 than in Corpus 2 – 0.0004.
The analysis of the representation of nonverbal behaviour in fiction was extended by considering word forms of the most frequent words, which are the same in each of two corpora. They are the following words and word forms: say*, v (said, v; say, v; saying, v; says, v); tell*, v (told, v; tell, v; telling, v; tells, v); talk*, v (talked, v; talk, v; talking, v; talks, v); speak*, v (spoke, n, spoken, v; speaking, v; speaks, v); sound*, v (sounded, v; sound, v; sounding, v; sounds, v); sound*, n (sounds, n); voice*, n (voices, n) for “paralanguage”; and go*, v (went, v; go, v; going, v; goes, v); come*, v (came, v; come, v; coming, v; comes, v); walk*, v (walked, v; walk, v; walking, v; walks, v); follow*, v (followed, v; follow, v; following, v; follows); turn*, v (turned, v; turn, v; turning, v; turns, v) for “moving”.
The following tables (Table 2, Table 3, Table 4, Table 5) contain quantitative and qualitative results of the research. Quantity and absolute frequencies of the words denote two types of nonverbal behaviour (paralanguage and moving) and two groups of meaning (descriptive, nondescriptive). Qualitative results are presented as a textual locale for each word form according to descriptive or nondescriptive meaning.
Descriptive meaning of nonverbal behaviour is defined as the meaning which contributes to characters’ nonverbal behaviour, e.g., the words denoting nonverbal behaviour have descriptive meaning if they are followed by some description of voice qualities or the way the movement is done; for example: to say coolly; to tell in flat voice; to go slowly; to turn minimally. The descriptive meaning of “moving” is direct and may not be followed by the adjective or the adverb which describes it, but it may indicate the direction: to come into the room; to go through a door; to turn onto the side.
Nondescriptive meaning of nonverbal behaviour is defined as meaning which contributes to textual coherence, e.g., the words denoting nonverbal behaviour have nondescriptive meaning if they are not followed by any description of voice qualities or the way the movement is done; for example, he said that …; he told us something …; I was going to see …; It would come soon … Nondescriptive meaning of “moving” is often indirect, metaphorical: to go mad; her drinking came; to follow the logic.
Table 2, Table 3, Table 4 and Table 5 contain a total quantity of words denoting two types of representation of nonverbal behaviour and two groups of meaning in corpora.
Descriptive meaning of nonverbal behaviour/quantity
Nondescriptive meaning of nonverbal behaviour/quantity
Paralanguage 43 ‘This isn’t exactly chatty,’ she said coolly … (p. 10). - 38 He said, ‘I like it when you say
brilliant.’ (p. 88) - 3 ‘Our cover,’ she kept saying in a loud whisper (p. 48). - 2
7
A man came out … and told me in a nervous, pleasant way that I
should wait (p. 54). - 4 He was going to tell me in his
own way … (p. 22). - 2
him, in that same flat voice, that all his climbing gear has been taken too (p. 93). - 1 419
the reviews … (p. 159) - 282 I don’t find this easy to say, but I’m deeply disappointed.’ (p. 23) - 97 Tony was saying, ‘You know where this all has to lead, don’t you?’ (p. 23) 29
clear she doesn’t believe him (p. 91). 11 281 … she ignored questions and told us nothing … (p. 67) - 128
… while I stood there watching him, wondering whether he was telling the truth (p. 40). - 24
122 So we talked of other … (p. 119) - 30 Talking, v 42
Talks, v 1 Speak, v* 82 Spoke, v 27 Spoken, v 9
Speak, v21 Speaking, v 15
Speaks, v 2 Sound, v* 37 Sounded, v 21
Sound, v 10 Sounding, v 3 Sounds, v 3 Sound, n* 18 Sound, n 16 Sounds, n 2 Voice, n* 38 Voice, n 31 Voices, n 7
Total (words and word forms of paralanguage): 1047 6
We spoke in identical tones, we were socially confident … (p. 30) 6 37
modest but it sounded dismissive… (p. 13) - 21 I said, hoping I didn’t sound like I was pleading (p. 88). - 10 …he couldn’t stop himself from sounding abject one moment and over-emphatic … (p. 149) - 3
18 …I thought I heard the sound of a voice (p. 173). - 16
muted sounds? (p. 111) - 2 36
affectless voice … (p. 93) - 29
Total (words and word forms of descriptive meaning of paralanguage): 147
‘But she’s very kind really and she’ll like talking to you (p. 145). - 42
76
spoken to my headmaster (p. 8). - 9
Everyone was speaking of ‘the crisis’ (p. 112) - 15
2
Total (words and word forms of nondescriptive meaning of paralanguage): 900 Turn, v* 135 Turned, v 91
Turn, v 28 Turning, v 12
Turns, v 4
Total (words and word forms of moving): 918
Total (words and ward forms of paralanguage and moving): 1965 Say, v* 302 Said, v 217
Say, v 54 Saying, v 27
Says, v 4 Tell,v* 126 Told, v 66 Tell, v 42
Tells, v 1
Total (words and word forms of descriptive meaning of moving): 539
Total (words and word forms of nondescriptive meaning of moving): 379 Total (words and word forms of descriptive meaning of paralanguage and moving): 686 Total (words and word forms of nondescriptive meaning of paralanguage and moving): 1279 Paralanguage 60 “Solar energy?” Beard said mildly (p. 27) - 52
He was starting to say conversationally (p. 108). - 4
… deep female voice behind him saying kindly … (p. 61) - 4 1 … she told him plainly to go away (p.38). - 1 242 She said she did not mind what he did (p. 9) - 165 … he had no idea what he wanted to say (p. 42). - 50 … he was saying, but it seemed too abstract (p. 162). - 23 ... she says she’s wrong (p. 195) - 4 125
Talk, v* 67 Talked, v 12
Talk, v 33 Talking, v 22
Talks, v Speak, v* 73 Spoke, v 34 Spoken, v 5
Speak, v 22 Speaking, v 11
Speaks, v 1 Sound, v* 12 Sounded, v 10
Sound, v 1 Sounding, v 1
Sounds Voice, n* 51 Voice, n 40 Voices, n 11 Sound, n* 36 Sound, n 35 Sounds, n 1 3 He had to talk fast (p. 140). - 1 … laughing and talking at a relaxed, normal pitch (p. 90). - 2 13 … Tom Aldous spoke with the lilting confidence of a prize pupil… (p. 28) - 12 … and speaking in a measured, husky tone… (p. 33) - 1
7 It always sounded like a lie (p.
63). - 10 … and then, determined to sound grave rather than querulous, he said… (p. 163). - 1 38 … though her voice was as bright as ever (p. 13). - 32
… the sound of children’s voices approaching … (p. 45). - 6 28 … from inside came a muffled sound of bare feet … (p. 24). - 27 … from the galley … came the smell of frying meat and garlic and the sounds of spoons … (p. 61) - 1
him something else … (p. 207) - 17 64 … he talked about his work and travels … (p. 164) - 12
60 When Beard’s turn came, he spoke to the point (p. 96). - 22
words he had ever spoken (p. 177). - 5
speak first (p. 43). - 22
public… (p. 128) - 10 He still speaks at conferences… (p. 259) - 1
1
When he heard himself sounding off, he was not at all convinced… (p.183). - 1 13 … she would hear his voice but not his words (p.17). - 8
8
above him … (p. 42) - 8
Total (words and word forms of paralanguage): 667 Total (words and word forms of descriptive meaning of paralanguage): 154 Total (words and word forms of nondescriptive meaning of paralanguage): 513
… Beard wondered … as he left one office and went glumly toward the next … (p. 31) - 66
alone … (p.26). - 60 … before going in he found a litter bin and disposed of the plastic bag (p. 81). - 41
96
parchment-faced fellow … (p. 105).
- 36
88
white, the red went down painlessly, like water, at least at first (p. 72).
18
… the demand for energy will go on rising as the world's population expands … (p. 173) - 24
124
Come, v (participle 2) 23 Go, v* 267 Went, v 84 Gone, v 29
Go, v 84 Going, v 68
Goes, v 2 Come, v* 220 Came, v 108 Come, v 54 Coming, v 31 Comes, v 4 Walk, v* 39 Walked, v 24
Walk, v 4 Walking, v 11
Walks, v
… long-running sinecures had recently come to an end … (p. 19) 10 I thought I’d come and have a “Come on, man. Let’s go!” (p. 54) look round (p. 43). - 25 - 29
Tarpin … was coming toward But he, Beard, had had many him with a firm stride (p. 45). - 20 affairs himself … and, probably it was coming to an end (p.92). - 11 … he'll be arrested if he phones … though at times he comes or writes or comes within 500 yards closer to being pathetic … (p. 260). of our house (p. 212). - 2 2
39 … he walked unhurriedly down the garden path … (p. 88). - 24
He asked if he could at least walk with her across the parks (p.
180). - 4 … and it seemed he was walking directly toward it now (p. 109). - 11 Followed, v 9
Follow, v 2 Following, v 10
Follows, v 1 Turn, v* 95 Turned, v 62
Turn, v 21 Turning 12
Turns
Total (words and word forms moving): 645
Total (words and word forms of
moving and paralanguage): 1312 1000 800 600 400 200 0 … so Beard followed a narrow concrete path … (p. 41) - 3 “I follow you,” Jan said (p. 54).
2
32 … he groaned and turned angrily on his side (p. 65). - 25
Total (words and word forms of descriptive meaning of moving): 352 … the sound of a hiss followed by a whiplike crack… (p. 154) - 6
63 … spoiled generation turned its backs on the fathers who fought the war (p. 60). - 37
By turning his shoulder into the room, Beard was able to prompt his host … (p. 135) - 10
Total (words and word forms of nondescriptive meaning of
moving): 293
Total (words and word forms of descriptive meaning of moving and paralanguage): 506
Total (words and word forms of nondescriptive meaning of moving and paralanguage): 806
The absolute frequencies of words according to types of representation of nonverbal behaviour and groups of meaning are manifested in Figure 3 (Corpus 1) and Figure 4 (Corpus 2).
900 Paralanguage 147 379 Moving
539 Nondescriptive meaning
Descriptive meaning Paralanguage 293
Moving Nondescriptive meaning
Descriptive meaning
Paralanguage is a type of nonverbal behaviour; it prevails in the group of nondescriptive meaning in both fictional texts. It means that a writer uses words denoting paralanguage to indicate textual coherence. The descriptive meaning of moving is significant for the characters’ description. The quantity of words belonging to nondescriptive meaning is almost the same in both Corpora. At the same time, the quantity of words used to describe paralanguage in nondescriptive meaning is two times larger in Corpus 1. The quantity of words denoting moving is approximately the same in two groups of meaning.
The absolute frequencies of words and word forms representing nonverbal behaviour in the texts are shown in Figure 5 (Corpus 1) and Figure 6 (Corpus 2). 263 281 419
Walk, v* Sound, n* Sound,v*
Voice,n* Follow, v* Speak, v* Turn, v* Talk, v*
Go, v* Come, v*
Tell, v* Say, v* 0 2 6 7 18 60 63 64 60 88 96
The words and the word forms of walk, v*, sound*, n, sound, v*, voice, v*, go*, v, come*, v are productive in creating descriptive meaning in fictional texts. The words and word forms of tell*, v, say*, v, talk*, v, speak, * v are not usually used for this purpose in both corpora.
Ranks and absolute frequencies of the words denoting representation of nonverbal behaviour in Corpus 1 and Corpus 2 are shown in Table 6.
The rank of the words denoting paralanguage and moving as nondescriptive meaning of paralanguage coincides in two text corpora. The words sound*, v, sound*, n, walk*, v denote only descriptive meaning, and the word talk*, v denotes only nondescriptive meaning in Corpus 1. The word walk*, v denotes only descriptive meaning in Corpus 2. The rank of the words denoting paralanguage and moving as descriptive meaning is different: only the word rank of goes*, v; come,* v coincides. There are the same word ranks of follow*, v (Corpus 1) and turn,* v (Corpus 2).
Computer-assisted textual research has a considerable effect in studies disclosing the meaning of fiction texts. The words, denoting nonverbal behaviour in I. McEwan’s novels Sweet Tooth and Solar are important in textual interpretation. Computation text analysis has served to exhibit two types of nonverbal behaviour and two groups of meaning which were defined by a set of quantitative features: absolute, relative frequency, and the rank of the words and word forms a writer uses to create the coherence or to describe nonverbal characters’ behaviour in fictional texts. Qualitative data was received due to fictional contexts interpretation.
Representation of nonverbal behaviour in case studies of I. McEwan’s contemporary fiction comprises the following most frequent words and word forms: say*, v; tell*, v; talk*, v; speak*, v; sound*, v; sound*, n; voice*, n to describe paralanguage; and go*, v; come*, v; walk*, v; follow*, v; turn*, v to describe moving. The words and word forms of walk, v*, sound*, n, sound, v*, voice, v*, go*, v, come*, v are usually used by the writers to describe characters’ nonverbal behaviour while providing fiction text coherence is mostly bounded to the words tell*, v, say*, v, talk*, v, speak*, v. The importance of paralanguage is determined by its absolute frequency of the word say*, v – 462 and 320 in Corpus 1 and Corpus 2 respectively. The total quantity of words and word forms denoting paralanguage and moving is 1965 in both corpora. The same rank index of the words which denote the nondescriptive meaning of paralanguage and moving in two text corpora manifests and proves the idea that coherence is a text-forming and stable category. The words that describe characters’ nonverbal behaviour contribute to creating dynamism in fictional texts.
The set of quantitative data of the present research is the ground for further development of software for linguistic tasks to study the author’s writing style. Supplemented by the data about collocations, obtained practical results might contribute to the study of corpus linguistics, in particular to compile a corpus digital dictionary of words and word forms of nonverbal behaviour represented in contemporary British fiction.
Intelligent Systems, COLINS 2021, Volume I: Main Conference, Lviv, Ukraine, 2021, pp. 799– 810 [11] O. Levchenko, N. Romanyshyn, D. Dosyn, Method of automated identification of metaphoric meaning in adjective + noun word combination (based on the Ukrainian language), in: CEUR Workshop Proceedings, Vol., 3286, Workshop Proceedings of the 8th International Conference on Mathematics. Information technologies. Education, MoMLeT&DC, 2019, pp. 3370-380. [12] D. Head, Ian McEwan: Contemporary British Novelists. Manchester University Press, Manchester, 2007. [13] B. Tonkin, Ian McEwan: Why I’m revisiting the seventies. The Independent, 2012. URL: https://www.independent.co.uk/arts-entertainment/books/features/ian-mcewan-why-i-mrevisiting-the-seventies-8076683.html [14] M. H. Eaves, D. Leathers, Successful Nonverbal Communication: Principles and Applications, 5th ed, Routledge, New York, 2018. doi. 10.4324/9781315542317. [15] C. Bowern, Computational phylogenetics, Annual Review of Linguistics 4 (2018) 281-296. [16] D. McIntyre, B. Walker, Corpus Stylistics: Theory and Practice, Edinburgh University Press,
Edinburgh, 2019. [17] E. Purssell, N. McCrae, Reviewing Quantitative Studies: Meta-analysis and Narrative Approaches, in E. Purssell, N. McCrae (Eds.), How to Perform a Systematic Literature Review, Springer, Cham, 2020, pp. 69-101. doi: 10.1007/978-3-030-49672-2_7. [18] M. H. Short, G. N. Leech (Eds.), Style in Fiction: A Linguistic Introduction to English Fictional
Prose, 2nd ed., Pearson ESL, London, 2007. [19] A. Gibbons, S. Whiteley, Contemporary Stylistics: Language, Cognition, Interpretation,
Edinburgh University Press, Edinburgh, 2022. doi.10.1515/9780748682782. [20] D. McIntyre, B. Walker, Corpus Stylistics: Theory and Practice, Edinburgh University Press,
Edinburgh, 2022. doi: 10.1515/9781474413220. [21] G. Ignatow, R. Mihalcea, An Introduction to Text Mining, SAGE Publications, Thousand Oaks, 2018. doi:10.4135/9781506336985. [22] J. Kuhn, Computational text analysis within the Humanities: how to combine working practices from the contributing fields? Lang Resources & Evaluation 53 (2019) 565–602. doi:10.1007/s10579-019-09459-3. [23] S. M. Rasinger, Quantitative Research in Linguistics: an Introduction, 2nd ed., Bloomsbury,
London, 2013. [24] N. Nørgaard, Multimodal Stylistics of the Novel: More Than Words, 1st ed, Routledge, New York, 2018. doi:10.4324/9781315145556. [25] M. L. Jockers, T. Underwood, Text-mining the humanities, in: S. Schreibman, R. Siemens, J .Unsworth (Eds.), A New Companion to Digital Humanities, Wiley-Blackwell, West Sussex, 2016, pp. 291-306. [26] J. C. Ho, Using computational text analysis with social media text, SAGE Research Methods
Cases, 2022. doi:10.4135/9781529603590. [27] E. Alsop, Making Conversation in a Modernist Fiction: Theory Interpretation Narrative, Ohio State
University Press, Ohio, 2019. [28] G. Liveley, Narratology, Oxford University Press, Oxford, 2021. [29] S. Statham, The year’s work in stylistics 2020, Language and Literature 30/4 (2021) 407-433.
doi:10.1177/09639470211056687. [30] T. Ogata, T. Akimoto (Eds.). Post-Narratology Through Computational and Cognitive
Approaches, IGI Global, 2019. doi: 10.4018/978-1-5225-7979-3. [31] J. Attard, The Connection between literature and aesthetics: is it problematic? Symposia Melitensia 14 (2018) 79-90. [32] B. Stopel, Aesthetic appreciation and the dependence between deep and surface interpretation,
Journal of Literary Theory, 14 (2020) 94-119. doi: 10.1515/jlt-2020-0006. [33] W. Bisschop, Interpretation and aesthetic appreciation, Journal of Literary Theory, 14 (2020) 1-9.
doi: 10.1515/jlt-2020-0001. [34] M. Danesi. Understanding Nonverbal Communication: A Semiotic Guide, Bloomsbury
Publishing, London, 2021. [35] O. Hargie, Skilled Interpersonal Communication: Research, Theory and Practice, 7th ed,
Routledge, London, 2021. doi: 10.4324/9781003182269. [36] X. Jiang, (Ed.), Types of Nonverbal Communication, IntechOpen, London, 2021. doi: 10.5772/intechopen.83004.