=Paper=
{{Paper
|id=Vol-3171/paper33
|storemode=property
|title=Quantitative Features of the Words Representing Nonverbal Behaviour in Ian McEwan’s Fiction
|pdfUrl=https://ceur-ws.org/Vol-3171/paper33.pdf
|volume=Vol-3171
|authors=Oksana Melnychuk,Nataliya Bondarchuk,Ivan Bekhta,Olena Levchenko
|dblpUrl=https://dblp.org/rec/conf/colins/MelnychukBBL22
}}
==Quantitative Features of the Words Representing Nonverbal Behaviour in Ian McEwan’s Fiction==
https://ceur-ws.org/Vol-3171/paper33.pdf
Quantitative Features of the Words Representing Nonverbal
Behaviour in Ian McEwan’s Fiction
Oksana Melnychuka, Nataliya Bondarchukb, Ivan Bekhtab,c, Olena Levchenko b
a
Rivne Medical Academy, Rivne, 33017, Ukraine;
b
Lviv Polytechnic National University, Lviv 79013, Ukraine
c
Ivan Franko National University of Lviv, Lviv 79000, Ukraine
Abstract
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) which disclose the peculiarities of
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 McEwan’s novels. Words denoting
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.
Keywords 1
Nonverbal behaviour, quantitative features, paralanguage, moving, textual coherence, Voyant-
Tools, textual analysis
1. Introduction
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 [1], B. Korte [2], J. A. Hall, M. L. Knapp
[3], and A. Kendon [4] review this wealth of nonverbal components on the basis of a large and growing
body of modern research in nonverbal behaviour. S. Johar [5] proves that the description of nonverbal
COLINS-2022: 6th International Conference on Computational Linguistics and Intelligent Systems, May 12–13, 2022, Gliwice, Poland.
EMAIL: melnychukox@gmail.com (O. Melnychuk); nataliia.i.bondarchuk@lpnu.ua (N. Bodnarchuk), ivan.bekhta@lnu.edu.ua (I. Bekhta);
levchenko.olena@gmail.com (O. Levchenko).
ORCID: 0000-0003-4619-363x (O. Melnychuk); 0000-0002-5772-8532 (N. Bondarchuk); 0000-0002-9848-1505 (I. Bekhta); 0000-0002-
7395-3772 (O. Levchenko).
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
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 [6]. In spite of the fact that a multitude of subjective convictions
determines readers’ understanding of a fictional text, there are factual premises that we study in terms
of text data to ground the set of quantitative features and make them complete and visible.
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 [7, 8], a British novelist, short-story writer, and
screenwriter whose restrained, refined prose style highlights the horror of his dark humour and deviated
subject matter. We consider the intended objective through quantitative (computational) and qualitative
(interpretational) textual analysis [9-11]. Empirical data is effective while being grounded on web-based
text analysis. Voyant Tools (voyant-tools.org), an open-source project, which is available through
GitHub, proves to be effective in the context of quantitative and qualitative analysis of fiction texts.
2. Study reasoning
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.
3. Research method
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 [6, 14, 21]. The method
was used to characterize the words of a specific fictional text in connection to its content.
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.
Table 1
Summary of research corpora
Corpus 1 Sweet Tooth Corpus 2 Solar
Total words 103,948 92,438
Total words exposed to 1965 1312
qualitative and
quantitative analysis
Unique word forms 15,093 16,420
Vocabulary density 0.145 0.178
Readability index 9.322 11.400
Average words per 14.7 18.3
sentence
The most frequent said (320); didn’t (206); like beard (494); said (217); like (159);
words in the corpus (206); thought (186); tom (185); time (139); thought (133); man
way (180); know (171); i’d (113); way (112); just (103); years
(159); time (151); knew (144); (100); away (99); came (108);
went (144); just (131); told knew (97); rest (96); know (92);
(124); it’s (123); room (123). room (92).
4. Theoretical linguistic background
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. Representation of nonverbal communication and nonverbal
behaviour in a fictional text
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.
5. Results and discussion
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).
Figure 1: Diagram of relative frequencies of the most frequent words in text segments of Corpus 1
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.
Figure 2: Diagram of relative frequencies of the most frequent words in text segments of Corpus 2
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.
Table 2
Quantity and absolute frequency of the words denoting paralanguage presentation in Corpus 1
Word / Descriptive meaning of Nondescriptive meaning of
absolute nonverbal behaviour/quantity nonverbal behaviour/quantity
frequency/
quantity
Paralanguage
Say, v* 462 43 419
Said, v 320 ‘This isn’t exactly chatty,’ she Tom had said he didn't want to see
said coolly … (p. 10). - 38 the reviews … (p. 159) - 282
Say, v 100 He said, ‘I like it when you say I don’t find this easy to say, but I’m
brilliant.’ (p. 88) - 3 deeply disappointed.’ (p. 23) - 97
Saying, v 31 ‘Our cover,’ she kept saying in Tony was saying, ‘You know where
a loud whisper (p. 48). - 2 this all has to lead, don’t you?’ (p. 23) -
29
Says, v 11 Though Monica never says so, it is
clear she doesn’t believe him (p. 91). -
11
Tell, v* 288 7 281
Told 132 A man came out … and told me … she ignored questions and told us
in a nervous, pleasant way that I nothing … (p. 67) - 128
should wait (p. 54). - 4
Tell, v 128 He was going to tell me in his He wouldn’t tell me what it was (p.
own way … (p. 22). - 2 12). - 118
Telling, v 24 … while I stood there watching him,
wondering whether he was telling the
truth (p. 40). - 24
Tells, v 12 She pauses, and then she tells She is sick of her life, she tells him,
him, in that same flat voice, that sick of being financially dependent …
all his climbing gear has been (p. 92) - 11
taken too (p. 93). - 1
Talk, v* 122 - 122
Talked, v 30 So we talked of other … (p. 119) - 30
Talk, v 49 I need to talk to Serena (p. 146). -
49
Talking, v 42 ‘But she’s very kind really and she’ll
like talking to you (p. 145). - 42
Talks, v 1 He watches her closely as she talks,
and knows that every word is a lie (p.
95). - 1
Speak, v* 82 6 76
Spoke, v 27 We spoke in identical tones, we She spoke of her Syrian doctor, I
were socially confident … (p. 30) - spoke of Jeremy Mott, but not of Tony
6 Canning (p. 31). - 29
Spoken, v 9 Within a week my mother had
spoken to my headmaster (p. 8). - 9
Speak, v21 He went to speak but was stuck for
words (p. 86). - 21
Speaking, v 15 Everyone was speaking of ‘the crisis’
(p. 112) - 15
Speaks, v 2 As he speaks to the desk sergeant,
he feels a bit of a cad or a snitch (p.
92). - 2
Sound, v* 37 37 -
Sounded, v 21 I mumbled something
modest but it sounded
dismissive… (p. 13) - 21
Sound, v 10 I said, hoping I didn’t sound like
I was pleading (p. 88). - 10
Sounding, v 3 …he couldn’t stop himself from
sounding abject one moment and
over-emphatic … (p. 149) - 3
Sounds, v 3 It sounds very promising (p.
166). - 3
Sound, n* 18 18
Sound, n 16 …I thought I heard the sound of
a voice (p. 173). - 16
Sounds, n 2 What then of my politely
muted sounds? (p. 111) - 2
Voice, n* 38 36 2
Voice, n 31 Yes, she agreed in her I thought I heard the sound of a
affectless voice … (p. 93) - 29 voice. 173 - 2
Voices, n 7 In low voices we talked office
gossip for the first quarter of an
hour (p. 68). - 7
Total (words Total (words and word forms Total (words and word forms of
and word forms of descriptive meaning of nondescriptive meaning of
of paralanguage): paralanguage): 147 paralanguage): 900
1047
Table 3.
Quantity and absolute frequency of the words denoting moving presentation in Corpus 1
Word / Descriptive meaning of Nondescriptive meaning of
absolute nonverbal behaviour/quantity nonverbal behaviour/quantity
frequency/
quantity
Moving
Go, v* 394 263 131
Went, v 144 Desolate, I went slowly along My light-headed alliterative prose
Great Marlborough Street (p. 27) - went down well (p. 10). - 26
118
Gone, v 35 ... and a half later, the men had Everyone had gone mad, so everyone
gone (p.17). - 8 said (p. 19). - 27
Go, v 109 … Tony asked me if I would go It was a matter of letting my eyes
for a longish walk in the woods (p. and thoughts go soft, like wax …(p. 9). -
17). - 64 45
Going, v 85 … she was in the crowd going I wasn’t going to see the New Year in
out through the door (p. 47). - 67 with you (p. 176). - 18
Goes, v 21 He goes upstairs and lies on the As far as hand-holding goes, it won’t
bed … (p. 94). - 6 be much of an imposition (p. 103). - 15
Come, v* 249 116 133
Came, v 116 I came out of the cubicle, … drinking came later (p. 108). - 69
splashed my face … (p. 102). - 47
Come, v 83 If he comes looking for you, His Foundation money hadn’t
you're to turn … (p. 168). - 49 arrived, but he was sure it would come
soon (p. 111). - 34
Coming, v 38 I heard Shirley coming and I knew I had an interview coming up
quickly put the pile in order … (p. … (p. 20) - 23
52) - 15
Comes, v 12 The person who comes in with … so the money that comes to me
a holdall and sets down … (p. 94). - each month is not simply an impersonal
5 … (p. 103). - 7
Walk, v* 84 84 -
Walked, v 41 We walked in silence. He didn’t
know what to say (p.40). - 41
Walk, v 20 It takes him an hour to walk
the mile to his house (p. 94). - 20
Walking, v We knew what we were
21 walking towards (p. 108). - 21
Walks, v 2 … Sebastian walks back
towards the Street … (p. 91) - 2
Follow, v* 56 22 34
Followed, v With the music fading behind There followed a session of small talk
27 me, I followed their directions … (p. 54). - 9
(p. 83) - 18 I didn’t follow the logic of this, but I
Follow, v 10 said nothing (p. 8). - 10
Following, v I started out following her The following day I arrived home in
18 path, the one she describes in her the cathedral close with all my
memoir… (p. 28) - 3 belongings (p. 28). - 15
Follows, v 5 She establishes that there is a
brother and follows him to London
… (p. 64) - 1
Turn, v* 135 54 81
Turned, v 91 Then he turned and glanced Lately the weather had turned mild…
round the room, looking for me… (p. 144) - 51
(p. 140) - 40
Turn, v 28 … he managed to turn onto his This in turn delays the repayment to
side (p. 73) - 7 Monica’s brother … (p. 93) - 21
Turning, v 12 …and I saw in front of me a … nothing resolved, thoughts still
restrained movement of heads turning, when I heard footsteps on the
tilting or turning minimally (p. 46) stairs (p. 117). - 8
-4
Turns, v 4 He turns and standing before The DI switches off the projector and
him … (p. 91) - 3 turns up the lights (p. 94). - 1
Total (words Total (words and word forms Total (words and word forms of
and word forms of descriptive meaning of nondescriptive meaning of moving):
of moving): 918 moving): 539 379
Total (words Total (words and word forms Total (words and word forms of
and ward forms of descriptive meaning of nondescriptive meaning of
of paralanguage paralanguage and moving): 686 paralanguage and moving): 1279
and moving):
1965
Table 4.
Quantity and absolute frequency of the words denoting paralanguage presentation in Corpus 2
Word/absolute Descriptive meaning of Nondescriptive meaning of
frequency / nonverbal behaviour/quantity nonverbal behaviour/quantity
quantity
Paralanguage
Say, v* 302 60 242
Said, v 217 “Solar energy?” Beard said She said she did not mind what he
mildly (p. 27) - 52 did (p. 9) - 165
Say, v 54 He was starting to say … he had no idea what he wanted
conversationally (p. 108). - 4 to say (p. 42). - 50
Saying, v 27 … deep female voice behind … he was saying, but it seemed
him saying kindly … (p. 61) - 4 too abstract (p. 162). - 23
Says, v 4 ... she says she’s wrong (p. 195) - 4
Tell,v* 126 1 125
Told, v 66 … she told him plainly to go He told himself that things are
away (p.38). - 1 often not as bad as you think (p. 131).
- 65
Tell, v 42 But he would tell no one (p. 58). -
42
Tells, v 1 His doctor tells him that not
thinking about that thing make it go
away (p. 260). - 1
Telling, v 17 She seemed on the point of telling
him something else … (p. 207) - 17
Talk, v* 67 3 64
Talked, v 12 … he talked about his work and
travels … (p. 164) - 12
Talk, v 33 He had to talk fast (p. 140). - 1 She had come to talk … (p. 47). -
32
Talking, v 22 … laughing and talking at a I’m talking to a lawyer in Oregon
relaxed, normal pitch (p. 90). - 2 (p. 252). - 20
Talks, v -
Speak, v* 73 13 60
Spoke, v 34 … Tom Aldous spoke with the When Beard’s turn came, he spoke
lilting confidence of a prize pupil… to the point (p. 96). - 22
(p. 28) - 12
Spoken, v 5 These half-truths were the best
words he had ever spoken (p. 177). - 5
Speak, v 22 Beard thought it important to
speak first (p. 43). - 22
Speaking, v 11 … and speaking in a measured, She was nervous speaking in
husky tone… (p. 33) - 1 public… (p. 128) - 10
Speaks, v 1 He still speaks at conferences… (p.
259) - 1
Sound, v* 12 7 1
Sounded, v 10 It always sounded like a lie (p.
63). - 10
Sound, v 1 … and then, determined to
sound grave rather than
querulous, he said… (p. 163). - 1
Sounding, v 1 When he heard himself sounding
off, he was not at all convinced…
(p.183). - 1
Sounds -
Voice, n* 51 38 13
Voice, n 40 … though her voice was as … she would hear his voice but not
bright as ever (p. 13). - 32 his words (p.17). - 8
Voices, n 11 … the sound of children’s His turn to listen to voices through
voices approaching … (p. 45). - 6 the wall? (p. 24) - 5
Sound, n* 36 28 8
Sound, n 35 … from inside came a muffled At that thought he heard a sound
sound of bare feet … (p. 24). - 27 above him … (p. 42) - 8
Sounds, n 1 … from the galley … came the
smell of frying meat and garlic and
the sounds of spoons … (p. 61) - 1
Total (words Total (words and word forms Total (words and word forms of
and word forms of of descriptive meaning of nondescriptive meaning of
paralanguage): 667 paralanguage): 154 paralanguage): 513
Table 5
Quantity and absolute frequency of the words denoting moving presentation in Corpus 2
Word/absolute Descriptive meaning of Nondescriptive meaning of
frequency/quantity nonverbal behaviour/quantity nonverbal behaviour/quantity
Moving
Go, v* 267 179 88
Went, v 84 … Beard wondered … as he left After two or three glasses of the
one office and went glumly toward white, the red went down
the next … (p. 31) - 66 painlessly, like water, at least at first
(p. 72).
18
Gone, v 29 His groin was so tender that he Even the hangers were gone (p.
waited until the others had gone 9). - 17
inside… (p. 69) - 12
Go, v 84 Beard preferred to go around … the demand for energy will go
alone … (p.26). - 60 on rising as the world's population
expands … (p. 173) - 24
Going, v 68 … before going in he found a I’m going to talk to Aldous, then
litter bin and disposed of the plastic I’m going to take him with me to
bag (p. 81). - 41 Design (p. 33). - 27
Goes, v 2 Honestly, it goes deeper … (p.
82). - 2
Come, v* 220 96 124
Came, v 108 And here he came, a gaunt The machine came to life at first
parchment-faced fellow … (p. 105). touch (p. 68). - 72
- 36
Come, v She had come to talk, not to … long-running sinecures had
(participle 2) 23 listen (p. 47). - 13 recently come to an end … (p. 19) -
10
Come, v 54 I thought I’d come and have a “Come on, man. Let’s go!” (p. 54)
look round (p. 43). - 25 - 29
Coming, v 31 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
Comes, v 4 … 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
Walk, v* 39 39
Walked, v 24 … he walked unhurriedly down
the garden path … (p. 88). - 24
Walk, v 4 He asked if he could at least
walk with her across the parks (p.
180). - 4
Walking, v 11 … and it seemed he was walking
directly toward it now (p. 109). - 11
Walks, v - -
Follow, v* 24 6 18
Followed, v 9 … so Beard followed a narrow … the sound of a hiss followed by
concrete path … (p. 41) - 3 a whiplike crack… (p. 154) - 6
Follow, v 2 “I follow you,” Jan said (p. 54).
2
Following, v 10 Within seconds he was bouncing During the following week, some
across the plain, following through commentators agreed with her (p.
the sight holes (p. 55). - 1 97). - 9
Follows, v 1 A confrontation follows (p. 86).
1
Turn, v* 95 32 63
Turned, v 62 … he groaned and turned angrily … spoiled generation turned its
on his side (p. 65). - 25 backs on the fathers who fought the
war (p. 60). - 37
Turn, v 21 Rather than turn and have his It would be difficult to turn her
face ripped away, he hunched his from this calm, seductive mode (p.
shoulders (p. 68). - 5 161). - 16
Turning 12 Hammer, turning to Beard, By turning his shoulder into the
looked like he was about to go room, Beard was able to prompt his
down on one knee (p. 247). - 2 host … (p. 135) - 10
Turns - -
Total (words Total (words and word forms of Total (words and word forms of
and word forms descriptive meaning of moving): nondescriptive meaning of
moving): 645 352 moving): 293
Total (words Total (words and word forms of Total (words and word forms of
and word forms of descriptive meaning of moving and nondescriptive meaning of moving
moving and paralanguage): 506 and paralanguage): 806
paralanguage):
1312
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
1000
800
539
600
379
400
147
200
0
Paralanguage Moving
Nondescriptive meaning Descriptive meaning
Figure 3: The diagram of absolute frequencies of words as to types of representation of nonverbal
behaviour and groups of meaning in Corpus 1
600 513
500
352
400
293
300
154
200
100
0
Paralanguage Moving
Nondescriptive meaning Descriptive meaning
Figure 4: The diagram of absolute frequencies of words as to types of representation nonverbal
behaviour and groups of meaning in Corpus 2
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).
Walk, v* 84
Sound, n* 18
Sound,v* 37
Voice,n* 2 36
Follow, v* 22 34
Speak, v* 6 76
Turn, v* 54 81
Talk, v* 122
Go, v* 131 263
Come, v* 116 133
Tell, v* 7 281
Say, v* 43 419
0 50 100 150 200 250 300 350 400 450
Nondescriptive meaning Descriptive meaning
Figure 5: The diagram of absolute frequencies of words and their word forms, which represent
nonverbal behaviour in Corpus 1
Walk,v* 39
Sound,v* 1 11
Sound,n* 8 28
Voice,n* 13 38
Follow,v* 6 18
Speak, v* 13 60
Turn,v * 32 63
Talk,v* 3 64
Go, v* 88 179
Come, v* 96 124
Tell, v* 1 125
Say, v* 60 242
0 50 100 150 200 250 300
Nondescriptive meaning Descriptive meaning
Figure 6: The diagram of absolute frequencies of words and their word forms, which represent
nonverbal behaviour in Corpus 2
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.
Table 6.
Ranks and absolute frequency of the words denoting representation nonverbal behaviour
Rank Corpus 1 Corpus 2
Nondescriptive Descriptive Nondescriptive Descriptive
meaning meaning meaning meaning
1 Say, v* 419 Go, v* 263 Say, v* 242 Go, v* 179
2 Tell, v* 218 Come, v* 116 Tell, v* 125 Come, v* 96
3 Come, v* 133 Walk, v* 84 Come, v* 124 Say, 60
4 Go, v* 131 Turn, v* 54 Go, v* 88 Walk, v* 39
5 Talk, v* 122 Say, v* 43 Talk, v* 64 Voice, n* 38
6 Turn, v* 81 Sound, v* 37 Turn, v* 63 Turn, v* 32
7 Speak, v* 76 Voice, n* 36 Speak, v* 60 Sound, n* 28
8 Follow, v* 34 Follow, v* 22 Follow, v* 18 Speak, v* 13
9 Voice, n* 2 Sound, n*18 Voice, n* 13 Sound, v* 11
10 Sound, v* - Tell, 7* Sound, n* 8 Follow, v* 6
11 Sound, n* - Speak, v* 6 Sound, v* 1 Talk, v* 3
12 Walk, v* - Talk, v* - Walk, v* - Tell, v* 1
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).
6. Conclusion
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.
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