Online Appendix 6
Content
This is an appendix to Bednarek, Monika (2008). Emotion Talk across Corpora. Palgrave.
Appendix to Chapter 6 of Emotion Talk Across Corpora (Appendix
6)
Contents:
A 6.1 Description of corpus (BRC baby)
2
A 6.2 Description of methodology
11
A 6.3 Affect sub-types in conversation sub-corpus of the BRC baby
13
A 6.4 Applications and implications
14
A 6.1
Description of corpus (BRC baby)
The BRC baby consists of about 20,000 words from the registers of conversation, news reportage, fiction and academic discourse. Sampling procedures are described in Bednarek
(2008: Chapter 5). More detailed information (including titles of books, type of sample, circulation size, level of difficulty, author gender, speaker age and occupation etc) is listed below:
Conversation (22,613 words)
Samples from 7 files:
KBC, part 1, 1-337
KBD, part 6, 1916-2243
KBP, part 5, 1233-2004
KD0, part 3, 125-346
KD8, part 15, 7154-7413
KE2, part 21, 3027-3281
KE4, part 2, 102-491
Further information on files from which samples were chosen:
KBC:
3019 words (AB) from 14 conversations recorded by ‘Audrey’ (PS1A9) between 2 and 9
April 1992 with 9 interlocutors, totalling 6341 s-units, 31,337 words, and 3 hours 38 minutes
41 seconds of recordings.
Speakers:
PS1A9 Audrey, 61, housewife, Lancashire
PS1AA Gordon, 61, teacher, Lancashire
PS1AB Margaret, 45, nurse, Lancashire
PS1AC Joan, 50+, clerk, Central Northern England
PS1AD Kevin, 29, computer engineer, Northern
England
2
PS1AE Carl, 31, pharmacist, Northern England
PS1AF None ? ? ?
PS1AG Elaine, 28, housewife, Northern England
PS1AH Iris, 60, housewife, Lancashire
KBD
2690 words (AB) from 24 conversations recorded by ‘Barry’ (PS03W) between 1 and 6 February 1992 with 10 interlocutors, totalling 9021 s- units, 58,087 words, and 5 hours 12 minutes
10 seconds of recordings.
Speakers:
PS03W Barry, 41, entertainments consultant, Central Northern
England
PS03X
Terri, 35, bar staff, Home Counties
PS03Y
Hugh, 30, bar staff, Irish
PS040
Alan, 38, security, Lancashire
PS041
None ? ? ?
PS042
Mark, 30, dj, London
PS043
Ken, 30, security, Lancashire
PS044
None, 35, housewife, Lancashire
PS045
Sergio, 9, student (state primary), Lancashire
KE 4:
2683 words (C1) from 22 conversations recorded by ‘Valerie’ (PS0WN) between 30 January
and ?? ?? 1992 with 7 interlocutors, totalling 3280 s- units, 15,170 words, and 1 hour 55 minutes 21 seconds of recordings.
Speakers:
PS0WN Valerie, 36, staff nurse (pt), Scottish
PS0WP Peter, 34, sales representative, Scottish
PS0WR Jackie, 8, student (state primary), Scottish
3
PS0WS David, 10, student (state primary), Scottish
PS0WT Dawn, 11, student, Scottish
PS0WU None, 40+, dentist, Scottish
PS0WW None, 50+, telephone engineer, Scottish
PS0WX Dougie, 37, sales representative, Scottish
KD0:
2951 words (C1) from 106 conversations recorded by ‘Kevin’ (PS0HM) between 29 November and 5 December 1991 with 14 interlocutors, totalling 13,948 s- units, 77,692 words, and 10
hours 39 minutes 22 seconds of recordings.
Speakers:
PS0HM Kevin, 41, draughtsman, London
PS0HN Paul, 12, student (state secondary), London
PS0HP Ruth, 40, teacher, ?
PS0HR Michelle, 29, local government officer, European (French)
PS0HS Eric, 74, retired, London
PS0HT Adrian, 40, salesman, London
PS0HU Karen, 41, secretary, London
PS0HV Andrew, 33, local government officer, London
PS0HW Lisa, 13, student, London
PS0HX Babs (aka mutty), 70+, retired, Lower South-west England
PS0HY Joy, 70+, retired, London
PS0J0
Michael, 15, student, London
PS1KN None ? ? ?
KE 2:
2623 words (C2) from 153 conversations recorded by ‘Terence’ (PS0W2) between 20 and 27
February 1992 with 10 interlocutors, totalling 10,080 s-units, 77,961 words, and over 12
hours 49 minutes 22 seconds of recordings.
4
Speakers:
PS0W2 Terence, 70, retired (headteacher), East Anglia
PS0W3 Richard, 44, fireman, Lower South-west England
PS0W4 Margaret, 70, retired, Irish
PS0W5 Lucy, 13, student, Lower South-west England
PS0W6 Holly, 13, student, Lower South-west England
PS0W7 Adrian, 13, student, Lower South-west England
PS0W8 Danielle, 13, student, Lower South-west England
PS0W9 Christine, 40, housewife, Lower South-west England
PS0WA Mima, 50, housewife, Lower South-west England
KD8:
2654 words (C2) from 31 conversations recorded by ‘Martine’ (PS0LK) between 12 and 20
March 1992 with 10 interlocutors, totalling 10,787 s-units, 76,445 words, and over 7 hours 15
minutes 1 second of recordings.
Speakers:
PS0LK
Martine, 25, senior technician, Welsh
PS0LL
Mike, 28, construction worker, Welsh
PS0LM Merielle, 55, housewife, Welsh
PS0LN
None, 45, pub landlord, Home Counties
PS0LP
Harold, 58, engineer, Welsh
PS0LR
Nora, 76, housewife, Welsh
PS0LS
Will, 45, civil engineer, Merseyside
PS0LT
Michael, 40, technical director, Home Counties
PS0LU
Jim, 27, technician, Home Counties
KBP:
5993 words (DE) from 15 conversations recorded by ‘Clarence’ (PS065) between 13 and 19
March 1992 with 4 interlocutors, totalling 5039 s- units, 27,179 words, and 2 hours 23 minutes
42 seconds of recordings.
5
Speakers:
PS065 Clarence, 65, retired, Lancashire
PS066 Nina, 67, retired, Lancashire
PS067 Nev, 72, retired, North-east Midlands
PS068 Lil, 70, retired, Lancashire
News reportage (18,164 words)
30 articles from seven files (no tabloids):
A1E 1-145
A1G 1-158
A1M 1-138
A1N 1-160
A7 S 1-159
AL5 (all)
AJG 1 (all)
AL5 and AJG (Social): 3004 words
(1) ‘Struggling dentists’ pull more teeth
(2) Misconduct case GP to appeal
(3) ‘The NHS is not for sale’
(4) Marital strife comes out in the wash
(5) CALLED TO ORDER
(6) Sex education for priests urged by Pope
(7) Homosexual prayer book to go on sale
A7S (Arts): 3003 words
(8) Forget Kylie — here come the crisp bags
(9) What’s going on
(10) Jackboots beneath the serge
(11) OPERA: Edward Greenfield finds the Medea at Covent Garden relentlessly
loud Cool, but not Callas enough.
(12) For third South arts QEH
6
A1N (Sports): 3058 words
(13) Cricket First Test: Azharuddin’s daring defiance
(14) Motor Racing: Fighting Senna refuses to succumb
(15) Golf: Calcavecchia digs in for consolation prize
(16) Rugby Union: The dye is cast for injured Richards
(17) Football: FA investigates crowd trouble
A1M (science): 2747 words
(18) The revolution the countryside needs: Christian Wolmar says Britain is lagging behind in setting up ‘telecottages’
(19) The busy sex life of the nice male
(20) Cool solutions for hot climates: David Spark looks at tropical temperature
controls for vaccines
A1G (report): 3145 words
(21) Tigrayans advance on a helpless Addis Ababa
(22) Tribalism and liberation meet at Transkei burial
(23) Swapo remains favourite to win the United Nations-supervised elections next
month
(24) Out of India: The snow-wreaths melt away in the heat of battle
(25) Russians send Kabul 2000 supply trucks
A1E (Commerce): 3207 words
(26) Latest corporate unbundler reveals laid-back approach: Roland Franklin, who
is leading a 697m pound break-up bid for DRG, talks to Frank Kane
(27) Square Mile: Stock Exchange takes the bull by the horns
(28) View from Manhattan: Airlines fly into regulatory storm
(29) USM: Remedial action at Lincat has results
(30) MB and Caradon stay quiet on bid rumours
Fiction (20,563 words)
Beginning samples from 10 files:
7
AB9 1-145
AC2 1-105
BMW 1-133
C8T 1-100
CB5 1-144
CFY 1-152
FAJ 1-142
G0S 1-101
H9C 2-168
HR9 1-175
AB9 (2058 words)
Death of a Partner, Neel, Janet, Constable Company Ltd, London (1991)
= beginning sample, medium circulation size, female author, medium level of difficulty
AC2 (2159 words)
Man at the Sharp End, Kilby, M, The Book Guild Ltd, Lewes, East Sussex (1991)
= end sample, medium circulation size, male author, medium level of difficulty
BMW (1998 words)
Folly’s Child, Tanner, Janet, Century Hutchinson, London (1991)
= beginning sample, high circulation size, female author, medium level of difficulty
C8T (2222 words)
Devices and Desires, James, P.D., Faber Ltd, London (1989)
= beginning sample, high circulation size, female author, medium level of difficulty
CB5 (2048 words)
Ruth Appleby, Rhodes, Elvi, Corgi Books, London (1992)
= middle sample, high circulation size, female author, medium level of difficulty
CFY (1999 words)
My Beloved Son, Cookson, C., Corgi Books, London (1992)
= middle sample, high circulation size, female author, medium level of difficulty
8
FAJ (2047 words)
Masai Dreaming, Cartwright, J., Macmillan Publishers Ltd, Basingstoke (1993)
= middle sample, medium circulation size, male author, high level of difficulty
G0S (2111 words)
Indigo, Warner, Marina, Chatto Windus Ltd, London (1992)
= middle sample, medium circulation size, female author, high level of difficulty
H9C (1933 words)
The Prince of Darkness, Doherty, P.C., Headline Book Publishing plc, London (1992)
= middle sample, medium circulation size, male author, medium level of difficulty
HR9 (1988 words)
They Came from SW19, Williams, Nigel, Faber Ltd, London (1992)
= end sample, high circulation size, male author, medium level of difficulty
Academic discourse (23,781 words)
Beginning samples from 10 files:
A6U 1-109
ACJ 1-101
ALP 1-101
AS6 1-80
EA7 1-93
EWW 40-139
FC1 1-83
FEF 1-121
FPG 1-101
HWV 1-93
A6U: 2411 words from:
‘Being Drawn to an Image’, Guy Brett, Oxford Art Journal (1991)
sample type unknown, from periodical, multiple authors, high difficulty
9
ACJ: 2666 words from:
Principles of Criminal Law, Andrew Ashworth, OUP, Oxford (1991)
= middle sample, from book, male author, high difficulty
ALP: 2285 words from:
‘A Non-punitive Paradigm of Probation Practice: Some Sobering Thoughts’, L.R. Singer,
British Journal of Social Work (1991)
= middle sample, from periodical, multiple authors, high difficulty
AS6: 2073 words from:
Tackling the Inner Cities, Ben Pimlott and Susanne MacGregor, OUP, Oxford (1991)
= beginning sample, from book, multiple authors, high difficulty
EA7: 2511 words from
France in the Making, 843-1180, Jean Dunbabin, OUP, Oxford (1991)
= middle sample, from book, female author, medium difficulty
EWW (without foreword: tribute): 2299 words from:
Matrices and Engineering Dynamics, A. Simpson and A.R. Collar, Ellis Horwood Ltd,
Chichester (1987)
= beginning sample, from book, multiple authors, medium difficulty
FC1: 2333 words from:
‘In re A DEBTOR (NO. 784 OF 1991) 1992 April 13’, J. Hoffmann, The Weekly Law
Reports, vol 3 (1991)
= sample type unknown, from periodical, author details unknown, high difficulty
FEF: 2124 words from:
Lectures on Electromagnetic Theory, L. Solymar, OUP, Oxford (1984)
= beginning sample, from book, male author, high difficulty
FPG: 2293 words from:
Design of Computer Data Files, O. Hanson, Pitman Publishing, London (1989)
= middle sample, from book, male author, high difficulty
10
HWV: 2786 words from:
‘Immunogenicity of a Supplemental Dose of Oral Versus Inactivated Poliovirus Vaccine’
The Lancet, London (1993)
= unknown sample type, from periodical, multiple authors, medium difficulty
A 6.2
Description of methodology
The data was analyzed and coded with the help of Altova XMLSpy 2007 (www.altova.com),
an XML editor software, which allows you to tag data with a number of attributes (Bednarek
2008: Chapter 5). Each emotion term was coded on nine linguistic variables:
1) Affect type
2) Affect trigger
3) Covert1 -overt affect
4) Emoter
5) Hypotheticality
6) Negation
7) Part of speech2
8) Valence
9) Speech act
Remarks on affect type, covert and overt affect and valence are made in Bednarek (2008:
Chapter 5), so that this section focuses on hypotheticality, negation, and speech act, with the
analysis of the remaining variables being relatively straight- forward and in no need of further
elaboration.
Hypotheticality
Under the heading of hypotheticality the analysis focused on whether an emotion was described as being experienced (in the past or present) in reality, or whether its experience was
predicted (in the future) or just hypothesized (in a possible world). Table A.21 shows typical
analyses of emotions as ‘hypothetical’, ‘will’ (future) or ‘real’:
11
Table A.21: Analysis of hypotheticality
Coding
Typical analyses
‘Hypothetical’
deontic and dynamic modality when referring to non-real, non-actualized emotion;
hypotheticality; intention etc: would/’d, could (past + future), should, can, (in order)
to, the purpose was to, have to, must be, might, in any desired order, ought to,
impossible to, if, as if, as often as she wished, had been about to, unless, wanting
someone to feel, for (‘in order to’), try to look like a man who enjoyed, designed to,
test whether, whether or not x is around to be impressed, the opportunity to, would
work for, I want to see…, inclinations towards, prevent, whatever you want
won’t, shall, ‘ll, will, … ahead
everything else, including evidentiality and epistemic modality, reported emotions,
e.g.: seem, evidence of, I bet, on the face of it, predict, obviously, I understand (that),
a message that, you don’t say you love, accuse of, perhaps, I thought you liked it,
apparently etc3
‘Will’
‘Real’
Negation
The categorization of negation relates to whether or not an emotion is negated. As noted in
Bednarek (2008: Section 5.3.2.2), there is a distinction in appraisal theory between negative
emotions (sad) and negated positive emotions (not happy), with the latter coded as ‘neg +
hap’ rather than ‘-hap’. Thus, ‘negated’ relates to grammatical negation (not, no, never etc)
whereas ‘non- negated’ includes morphological (negative prefix (un-, in-, dis-) or suffix
(-less)), and lexical negation (such as disinclined, refuse, reluctant, dislike, doubtfully). On
emotion words and negation see also Nöth (1992).
Speech act
Finally, the category that I have labelled ‘speech act’ here purely relates to whether the emotion is questioned (e.g. do you want x, do you love me?) or asserted (e.g. I am furious). This
does not in fact correspond to whether the speech act as such is a question or not; for instance
examples such as Why does it interest you? What do you want? are questions but contain asserted emotions (in contrast to Does it interest you? Does she want to collect you?). Only if
the experience of the emotion is questioned to some extent was this coded as ‘question’ rather
than ‘statement’ (tag questions were not counted as questions either, relating to mitigation/hedging). ‘Question’ also includes reported questions, for example consider whether they
really wanted to.
12
A 6.3 Affect sub-types in the conversation sub-corpus of the BRC baby
120
Misery
100
Non-desire
Displeasure
80
Interest
Antipathy
60
Surprise
Cheer
40
Pleasure
Disquiet
20
Affection
0
Desire
KBC
KBD
KBP
KD0
KD8
KE2
KE4
Desire Affection Disquiet Pleasure Cheer Surprise Antipathy Interest Displeasure Non-desire Misery
KBC
44.4
16.6
11.1
11.1
5.5
11.1
0
0
0
0
0
KBD
15.4
57.7
0
3.8
0
0
7.7
7.7
7.7
0
0
KBP
37.5
20.8
0
4.2
4.2
12.5
4.2
0
12.5
4.2
0
KD0
41.2
11.8
35.3
0
0
0
0
0
11.8
0
0
KD8
47.1
17.6
11.8
0
0
0
11.8
0
5.9
0
5.9
KE2
64.7
35.3
0
0
0
0
0
0
0
0
0
KE4
64.3
21.4
0
7.1
0
0
7.1
0
0
0
0
(Figures refer to percentages of affect sub-type with respect to all emotion terms in given file; for instance,
44.4% of all emotion terms in KBC realize Desire)
A 6.4 Applications and implications
In Bednarek (2008: Chapter 6) I noted that the findings might have some implications for:
•
the application of appraisal theory;
•
the modelling of probabilistic (intra- and inter-) register variation;
•
natural language processing (automated register recognition, parsing of affect);
•
language teaching and lexicography.
The following sections provide a more detailed discussion of these aspects.
13
Appraisal theory
In terms of appraisal theory, there is a need for discourse analysts to try out the new classificatory system of affect (including the nine variables identified above), in order to test its
applicability, its advantages and disadvantages. More (theoretical) research is also called for
in the areas of:
•
Surprise and counter-expectation: should this be established as an evaluative (appraisal) system in its own right?
•
Types of covert affect: what is the usage and patterning of, and difference between
nouns such as worries, disappointments, adjectives such as worrying, and adverbs
such as sadly? Are there also verbs that indicate covert affect?
•
Authorial vs. non-authorial appraisal: does non-authoria l appraisal involve intersubjectivity (engagement)?4
•
Appraisal and grammatical metaphor: when do appraisal expressions construe modality, when affect/engagement (see Martin 1992, 2000b, Martin & White 2005: 54-56)?
•
What is the role of talk about not experiencing an emotion, rather than talk about
experiencing an emotion? Galasinski notes that in his data there is a ‘narrative awareness of the fact that certain events in people’s lives […] might be associated with certain emotional experiences and the y explicitly acknowledge this by denying ha ving
these experiences.’ (Galasinski 2004: 83). How is this related to appraisal? It might
also be interesting to examine talk about hypothetical emotions (Galasinski 2004: 84).
Since the BRC baby was coded for hypotheticality (see above), this aspect can easily
be investigated in the future.
•
Affect and intensity (graduation): how are degrees of intensity conveyed in emotion
talk? This is likely to be a huge research project: ‘Studying language intensity in emotion talk will be challenging because modifiers of emotion terms, inflectional and intonational changes, and even exclamations will likely need to be considered’ (Anderson & Leaper 1998: 443). For already existing studies on intensity/involvement see
Bednarek (2008: Section 1.3).
•
Appraisal and polyphony (Downes 2000): what is the interplay between different
evaluative (appraisal) systems and can a typological description capture this?
•
Appraisal and textual structure: how do the prosodic structure of interpersonal meaning, and the periodic structure of textual meaning interact? How is evaluative meaning
14
distributed in texts (elaborating on research by Martin 1992, 1997, 2002a, 2004,
Macken-Horarik 2003: 317, Martin & White 2005: 85-89)?
Current research addresses these issues in more detail (e.g. Bednarek 2007). It must also be
pointed out that the focus of Bednarek (2008) was on emotion talk (the use of emotion terms)
rather than emotional talk (compare Bednarek 2008: Chapter 1), though there is a whole range
of resources for emotional talk without the use of emotion terms: ‘An explicit emotion
vocabulary is not necessary for powerful displays of emotion with language in its full
pragmatic environment’ (Goodwin & Goodwin 2000: 254).
Modelling register variation
Findings about frequencies in text are useful for a variety of reasons, providing us with information about the semiotic system itself (Halliday 2005: 45) and the modelling of register
variation. The establishment of probability profiles (in Bednarek 2008: emotion profiles) has
implications for ‘at least five areas of theoretical enquiry: developmental, diatypic, systemic,
historical and metatheoretic.’ (Halliday 2005: 73). Knowing about the frequency of words is
also important in various areas of language teaching, for example the design of curricula, the
writing of materials and the testing of language proficiency (Leech et al 2001: ix), and in the
fields of natural language processing, linguistics, psychology, and cultural studies (Leech et al
2001: x). Leech et al also point out that
for the various uses of frequency information mentioned earlier, particularly in the
educational arena, we need to reckon on different frequency profiles for different varieties of the language. The idea that one monolithic frequency list for the whole
language can satisfy all needs is, of course, unrealistic. (Leech et al 2001: xi).
As we have seen in Bednarek (2008), there is some variation in the emotional profiles of the four
registers investigated here. If we want to include such register variation in a description of affect,
how can we model it? SFL lends itself quite well to probabilistic modelling, since it recognizes
that there are probabilistic tendencies in language, and recently several studies have addressed the
issue of modelling co-selection or intersections of systems (e.g. Matthiessen 2006, Tucker 2006).
It is suggested that system networks can be used to represent probabilistic tendencies:
15
the system network can represent not only the dependency of one system on another
or others, but also any probabilistic correlation between any two features anywhere
in the network. We are thus able to represent the relationship between choices in the
various systems, such as transitivity, tense, mood etc., which are manifested through
syntagmatic co-occurrence. […] Importantly, the notion of pre-selection is introduced into the grammar. The selection of any feature, or combination of features,
may lead to the pre-selection of subsequent features, either in terms of absolute preselection or of the setting of probabilities on the features in the system(s) in question. (Tucker 2006: 92; emphasis in original).
The modelling of probabilistic variation is arguably an integral part of any description of the
lexico-grammatical resources of a language (Matthiessen 2006). Additionally, it may provide
input for any future computational linguistic modelling. Even though networks are specifically
used in SFL, they also represent a user-friendly device to taxonomize probabilistic tendencies of
corpus-linguistic (CL) evidence regardless of the theory researchers adhere to. For examples of
probabilistic modelling in SFL see Tucker (2006), and Matthiessen (2006). For a discussion of
SFL vs. CL principles see Hunston & Thompson (2006), especially Hunston (2006). However,
more research is necessary on intra-register variation (see Bednarek 2008: Chapter 6 for
preliminary comments). The crucial question is whether there are more similarities between texts
across registers than between texts within a certain register. This should be determined with the
help of sophisticated statistical measures (Biber 1989, Kilgarriff 2001).
Natural language processing
Automated register recognition
Can the findings about the frequency of emotion terms in different registers help automated
register recognition? A good overview of previous research and criteria on differentiating
between text types is given by Stubbs & Barth (2003: 79). It seems likely that in as far as
emotion terms are content words, they are at least partly dependent on topic, and ‘may
therefore be frequent in an individual text, but absent in another text from the same text-type.’
(Stubbs & Barth 2003: 67). Further, the kind of analysis that I have undertaken was meaningsensitive, and cannot yet be automated. More promising are word-chains (Stubbs & Barth
2003: 62) or lexico- grammatical patterns/local grammars as described in Chapters 3 and 4.
16
Parsing affect
As Hunston & Sinclair propose, parsers can be developed on the basis of local grammars that
can automatically extract information from texts (e.g. the automatic retrieval of definitions
from texts) (Hunston & Sinclair 2000: 82). A parser that could be developed on the basis of
the description in Bednarek (2008: Chapter 3),5 i.e. that is programmed with patterns that
allow it to automatically produce a simple table or list of emoters, emotions and triggers from
texts, might be useful enough (though it is important that negation be included to capture the
distinction between love and no/not love – but this is easy when pre-processed corpora are
used). Although the relations between emoter, emotion and trigger may vary according to the
particular emo tion involved, the human analyst can decide what these relations are, on the
basis of the out put of the parser and his/her linguistic and non-linguistic knowledge. If the
parser output is emotion = love, trigger = you, the relation is one of direction; whereas if the
parser output is trigger = the results, emotion = surprise, the relation is one of cause.
Ultimately, however, more detail (though not all) that can be found in FrameNet could be
added to parsers. For example, if the degree element could automatically be parsed by the
software, it would be possible to produce a list of emoters, emotions, triggers and the degree
of emotion involved. In terms of appraisal theory, the results of this parsing would show both
affect and graduation – two of the sub-systems of appraisal (Bednarek 2008: Section 1.4).
More and more details could gradually be included (from FrameNet and other corpus research) to make the parser more and more sophisticated (to enable it to deal with variations of
patterns, e.g. pseudo-clefts, changed word-order and so on. Compare Francis et al 1996: 611615).
Since I am neither a computational linguist nor an AI researcher, I cannot authoritatively
discuss how easy or difficult the development of such a parser could be (for a discussion of
some issues regarding a local grammar of evaluative adjectives see Hunston & Sinclair 2000:
82, and Hunston 2002: 180). However, the following factors could cause some problems for
such an application:
•
Patterns come in different forms and are changed by processes such as clefting, fronting, passivization etc (see Francis et al 1996: 611-615, Hunston & Francis 2000).
•
The difference between undirected and directed affect patterns is superficial: triggers
of presumably undirected affect patterns may be explicitly stated in the context or inferable by readers/hearers.
17
•
Patterns can have different mappings depending on the lexical item or meaning group
involved (see also Hunston 2003: 7, Hunston & Sinclair 2000: 88), e.g.:
(n) V n:
(i) emoter emotion trigger (I admired them like I admire Tom Wolfe, BNC, CHA
2382 )
(ii) trigger emotion emoter (Her reaction surprised me, BNC, H0D 2326 )
ADJ for n:
(i) emotion for trigger (Regan, anxious for an alternative theoretical platform from
which to put into orbit his conviction, BRC, CM8 491; enthusiastic for young
athletes to do well, BRC, CH6 370; desperate for money, BRC C8E 198, anxious
for her sons, BRC, CCD 2410; willing for more fun, BRC, FPF 2661)
(ii) emotion for empathy target (I am happy for him, BNC, H8G 790; I am delighted
for you, BRC, AE0 2465; I’m pleased for you, BRC, KCX 644; I’m frightened for
him, BRC, CH3 3192; I am very disappointed for Jimmy, BNC, AHC 662).
ADJ n
(i) emotion emoter (anxious faces, BRC, H8A 1451)
(ii) emotion trigger (sad sound, BNC, G0Y 2062)
Thus, the order of the elements emoter, emotion, and trigger varies with directed affect
patterns (depending on the verb), and the pattern ADJ for can express both directed
(with trigger) and undirected affect (with empathy target). In such cases, the parser has
to be given lists of lexical items that occur with each mapping. With the pattern ADJ
n, the matter is more complex, since it seems to depend on the noun whether directed
or undirected affect is concerned. Research would be necessary to determine which
kinds of nouns typically function as emoters and which as triggers.6
•
The local grammar description involves patterns (e.g. passives PV by n, basic patterns
such as ADJ n) that also occur in areas outside affect:
PV by n
He … was respected and admired by all of his colleagues in the forces (FrN) (affect)
PV by n
After their divorce Jane worked on, helped by students at the growing research
station. (BNC, A7D 448)
ADJ n
They were admitted by a surprised servant (BRC, CD2 1395) (affect)
ADJ n
The old man and the girl are listening attentively. (BNC, A04 1362)
Again, the parser would have to be given lists of lexical items inscribing affect to distinguish such patterns.
18
•
In some cases, it depends on the context whether when-clauses and because-clauses
can be mapped as trigger or not. In examples 1-2 below the clauses seem to express
circumstances of time with respect to an emotion directed by an emoter at a trigger,
rather than triggers of emotions themselves (as in examples 3-5):
(1)
When she was quite small [circumstance] she had been surpris ed [emotion] to see the woman
carrying a folded table and a Gladstone bag [trigger]. (BRC, AEA 176)
(2)
When he looked up [circumstance], he seemed surprised [emotion] to find Lacuna still staring
at him [trigger]. (BRC, F9X 3448)
(3)
When presently the young person came in [trigger], he was definitely surprised [emotion].
(4)
She was asked to study, so she did, and seemed surprised [emotion] when her application
produced good marks [trigger]. (BRC, HWE 2039)
(5)
He looked even more surprised [emotion] when Elaine walked into the lounge with two men
[trigger]. (BRC, FAB 2451)
Similarly, because-clauses can either refer to the triggers of an emotion (you are disappointed because something happened), as in examples 6-7 or they can be used to
elaborate why someone feels disappointed at a trigger (examples 8-9):
(6)
I’m really disappointed [emotion] cos I don’t think my mum will let me stay now! [trigger]
(BRC, KCE 251)
(7)
I was really disappointed [emotion] cos erm I didn’t get that one from Mencap [trigger]
(BRC, KCP 5756)
(8)
[…] last time on a, I was really disappointed [emotion] I’d been refused the course [trigger] cos
I thought oh, it’ll be right up my street that (BRC, KBW 15192)
(9)
In fact, we’re disappointed [emotion] with it [trigger] cos usually we have vegetables out of
our own garden all the year round (BRC, KC0 5317)
Since it seems that when- and because-clauses are only triggers when no other triggers
are identifiable in the sentence (examples 3-7), the parser must be trained to include
when- and because-clauses as triggers only if no other triggers have already been
parsed.
However, if a way can be found of solving these (and perhaps other) problems (maybe
through combining a thesaurus-based seed list of emotion terms with information on patterns),
a parser could be developed that automatically retrieves inscribed affect from texts, listing
emoters, emotions and triggers. As John Patrick (p.c.) has noted, sentiment classification is of
19
growing interest in computational linguistics, with an increase in publications about detecting
and classifying sentiment and subjectivity in discourse (compare also Taboada & Grieve
2004, Whitelaw et al 2005, Bloom et al 2006), and many important conferences have been
held on this topic. The analysis of appraisal has a wide-range of applications, for example
‘data and web mining, market research, and customer relationship management’ (Whitelaw et
al 2005: 1). Some of these approaches already use appraisal theory and include different
appraisal attributes such as attitude type, orientation, force, polarity and target type (Taboada
& Grieve 2004, Whitelaw et al 2005, Bloom et al 2006, focussing on adjectives).
Language teaching and lexicography
Language teaching
Pattern-based descriptions of local grammars are also potentially interesting for language
teaching purposes. Patterns, as Hunston (2002: 173-174) notes, can be taught to students to
improve both fluency and accuracy. The particular advantage of a local grammar approach to
patterns lies on the one hand in their transparent, very ‘user- friendly’ category labels, i.e. semantic labels such as emoter, emotion, and trigger that directly reflect the discourse function
of sentence elements (which the traditional categories of subject, verb etc do not). On the
other hand, the organization of local grammars according to meaning means that exercises can
be developed that cover a particular area of meaning – one that might be important to the particular students involved. Hunston (2002) discusses some examples of teaching methods
appropriate to pattern teaching (without a specific discussion of local grammars).
Lexicography
A number of questions for lexicography seem to arise from the findings outlined in Bednarek
(2008). The first concerns the variability of lexico-grammatical patterns investigated in Bednarek (2008: Chapter 4). While some of these terms show a very strong preference for interregister stability, others demonstrate inter-register variation. In other words, with some
20
terms there is not much variation in terms of the most common L1 or R1 collocate, whereas
others do vary across registers.
Table A.22 below sums up the most frequent L1 collocates of the analyzed emotion terms in
the four corpora. As we can see, enthusiastic, hate (V) and delighted (and perhaps also
admire, surprised, and surprise (V)) exhibit relatively stable L1 collocates across at least
three corpora (red), whereas other emotion terms are more varied (e.g. surprise (N), anxious,
disappointed, impressed, pleased, willing, affection, frightened). That is, we can make a
distinction between lexico-grammatically stable (LG-stable) and lexico- grammatically
volatile (LG-volatile) emotion terms.
Table A.22 L1 collocates of emotion terms across corpora
L1
A
C
N
F
surprised
be
‘m, (not)
be, (was)
was, (be)
surprise (N)
no
a
a
in
surprise (V)
(not)
n’t, (it)
not
to
admire (V)
to
NN (I, and)
I, (to)
to
anxious (A)
was
NN (was)
is
was
disappointed
be
(a) bit
be, very
was
enthusiastic
an
NN (very)
an
an
impressed
be
very
so
was
pleased
be
very
very
was
willing
be
are/re
be
was
affection
and
NN (had, no)
with, and, ‘s
of, with
hate (V)
NN (I)
I
I, (he)
I, (she, he)
delighted
NN (be)
was
was
was
frightened
NN (too, BECOME) was
are
was
hate (N)
NN
NN
of
NN
Considering only LG-volatile terms, news reportage shares collocates with conversation (a
surprise, very pleased) (pink), with academic discourse (be disappointed, be willing, and
affection) (green) and fiction (with affection) (blue). Conversation shares two L1 collocates
with news reportage (as mentioned), and one with fiction (was frightened) (grey), but none
with academic discourse. Fiction shares collocates with news reportage and conversation (as
noted), and with academic discourse (was anxious) (yellow).
Moving on to R1 collocation, Table A.23 (on the next page) sums up their distribution across
the four corpora.
21
Table A.23 R1 collocates across corpora
R1
A
C
N
F
surprised
that, by, (to)
if
to, (at, if)
to
surprise (N)
to, (that)
for
to, (that)
to, (and)
surprise (V)
us
me
the
me, him, (her)
admire (V)
the
NN (it)
the
the
anxious (A)
to
NN (to)
to
to
disappointed
by, with, that
with
by
that, (when)
enthusiastic
about
NN (about)
about
about
impressed
by
with
by
by
pleased
to
with
to
to
willing
to
to
to
to
affection
and
NN
for
for
hate (V)
NN (by, the, their, it, to
the, (to)
the, (to, it)
to)
delighted
NN (with)
to
to, (with)
to
frightened
NN (to)
of
of
of
hate (N)
NN
NN
NN
for
As becomes apparent here, three emotion terms are very much LG-stable (collocates shared
by all registers): anxious (to), enthusiastic (about) and willing (to). An additional eight emotion terms are relatively LG-stable (sharing R1 collocates among three registers: either all
written registers or all except academic discourse): surprised, surprise (N), admire (V),
impressed, pleased, hate (V), delighted and frightened. Other than that, we find LG-volatile
terms such as surprise (V) and disappointed. Concerning these, news reportage shares one
collocate with conversation (surprised if), and two with academic discourse (surprise that,
disappointed by) and fiction (affection for, hate the). Conversation (apart from sharing with
news reportage) also shares two collocates with fiction (surprise me, hate it), and one with
academic discourse (disappointed with). Fiction shares with conversation and news reportage
(as noted), and also has one collocate in common with academic discourse (disappointed
that). Summing up all shared collocates, the picture looks as Table A.24 (on the next page)
visualizes. We can see that academic discourse and news reportage are most similar (five
shared collocates). Conversation and news reportage, conversation and fiction, and news
reportage and fiction are also quite similar, sharing three collocates, respectively. This is
perhaps best explicable by the fact that both news reportage and fiction contain dialogue
(quotations by news actors and character dialogue) which mimic conversation, whereas both
22
also involve descriptions of emoters (news actors or characters). Furthermore, the news story
is, after all, also a particular type of story/narrative. Finally, fiction and academic discourse
share two collocates, and conversation and academic discourse share only one,7 being
relatively dissimilar (compare also the findings of the large-scale quantitative analyses in
Chapter 2).8
Table A.24 Shared L1 and R1 collocates across corpora
No of shared L1 and R1 collocates (where variation occurs)
Academic
Conversation
News
aaa(L1) aa(R1)
aaa(L1) aa(R1)
aa (L1) a(R1)
aa(L1) a(R1)
a(L1) aa(R1)
a(L1) aa(R1)
a(L1) a(R1)
a(R1)
a(R1)
Fiction
a(L1) aa(R1)
a(L1) aa(R1)
a(L1) a(R1)
What are the implications of these findings for lexicography? If senses of words differ in
various corpora (Kilgarriff 1997b: 108), and if lexico-grammatical patterns are registersensitive, we could develop dictionaries that reflect this. This could take the form of an
electronic dictionary (with a query system similar to that described by Teubert 2004) that includes five different dictionaries: one ‘general’ dictionary that lists meanings and lexicogrammatical patterns which are common and stable across registers, and four ‘register’
dictionaries (one each for conversation, news reportage, fiction, and academic discourse) that
list meanings and lexico-grammatical patterns that are common in and characteristic for the
respective register. The general dictionary should ideally include frequency bands for each
register (note that the Longman Dictionary of Contemporary English (LDOCE) already makes
a difference between written and spoken language frequency). Importantly, all ‘subdictionaries’ should include detailed information for meanings, patterns and pragmatic functions as well as authentic examples in the given register. Current dictionaries and other learner
sources include a lot of corpus-based information about frequency (Neale 2006: 149-155) and
collocation but this is not always referred to and can then remain relatively worthless to the
user (Sinclair 2004c: 143). And some corpora that are the sources of dictionaries consist
largely of media language such as the Bank of English (Mahlberg 2004: 119), which has some
implications on the resulting description of English.
In other words, what I propose is that the approach that Biber et al (1999) have taken
with respect to grammar can and should be extended to dictionaries, especially in view of the
more or less unlimited space available when they are produced in electronic rather than in
printed form. Thus, when learners read a newspaper or a novel, they can choose the ‘news
23
dictionary’ or the ‘fiction dictionary’; when they want to focus on spoken conversation, they
use the ‘conversation dictionary’; and academic learners will need mostly the ‘academic
dictionary’.9
Other potential applications
Other potential applications of some of the findings in Bednarek (2008) include the development of guidelines or exercises for fiction writing in English, with respect to characterization
and the portrayal of character emotion. It wo uld also be possible to use the affect classification in Chapter 5 for a character analysis à la Toolan (2001). In analogy to Toolan’s proposal
for a character-trait inventory, we could make use of an emotional response inventory. One of
Toolan’s character-trait categories is +/- emotional, and this could be elaborated to incorporate the kind of emotionality that is present or absent (Table A.25 below). In so doing, we can
build up an exact emotional picture of a character, contributing to the analysis of
characterization in works of fiction.
Table A.25
P = affect type present
O = affect type absent
+ = positive affect sub-type present
- = negative affect sub-type present
un/happiness
affection/antipathy
cheer/misery
in/security
quiet/disquiet
trust/distrust
dis/satisfaction
interest/ennui
pleasure/displeasure
dis/inclination
desire
non-desire
surprise
Character 1
P
+ (affection)
- (misery)
O
O
O
P
P
+
P
Character 2
O
O
O
P
O
O
O
O
O
O
-
Character 3
P
- (antipathy)
- (misery)
O
O
O
P
+
P
P
Character 4
O
O
O
P
+
O
O
O
P
Character 5
P
- (antipathy)
+ (cheer)
P
+
O
O
O
P
+
-
Finally, another application outside linguistics concerns the frequency findings of emo tion
terms in Bednarek (2008: Chapter 2). Rather than basing their analyses on elicited or freelisted emotion terms, psychologists and anthropologists could work with those emotion terms
that are most frequently used in conversation.
24
Notes
1
With respect to covert affect note also that not all terms that are derivationally related to
emotion terms should be included as covert affect. With some, the emotional meaning
has been bleached to a large extent; e.g. pity in It is a pity that… should not be coded as
covert affect but rather as judgement even though it shares patterns with covert affect.
Examples of terms that were excluded from the analysis of covert/overt affect are (Table
N.1):
Table N.1: Excluded terms
Excluded
dazzling, nae bother, was a real stunner, jarring, trouble, aspirant
from
members, x is a pain, jollification, buoyant markets, striking,
(covert/overt) strikingly, x was found wanting, pleasant, pleasure, pleasurable,
affect
2
awesome, preoccupation, delightful, their own interests
When analyzing part of speech, a rough distinction was made between the POS categories adjective – adverb – verb – noun – other (relating to idioms). In order to facilitate the
analysis, -ed and -ing verb participles (such as impressed by, admiring, dazzling,
cheered, brightening, much-loved, fed-up with, troubled by, tempted to) were automatically coded as adjectives (compare Bednarek 2008: Chapter 3, but see Osmond 1997:
112).
3
Would you like was analyzed as ‘real’ desire rather than ‘hypothetical’ affection.
4
The terms authorial (1st person) and non-authorial (2nd & 3rd person) affect are suggested
by White (2001a). It becomes clear from White’s discussion that the distinction has to do
with sourcing and the claiming or transferral of responsibility:
•
With authorial affect, ‘the writer is the source of the emotion by which the evaluation
is conveyed and hence takes some responsibility for that evaluation’ (White 2001a: 6);
•
With non-authorial affect, ‘[t]he writer presents herself as merely reporting on the
emotional reactions [of others]’ (White 2001a: 6, emphasis mine); ‘It is, in a sense, an
attributed evaluation, responsibility of which has been transferred to an external
source’ (White 2001a: 6, emphasis mine).
For a discussion that relates non-authorial affect to engagement (attribute: acknowledge)
see Bednarek (2007).
25
5
The approach used in Bednarek (2008: Chapter 3) for the description of affect patterns
was more detailed than local grammar (and less ‘strict’ about the notion of patterns), but
less detailed than FrameNet. It used the local grammar approach to functionally organize
and describe one area of meaning (affect), and the FrameNet approach to include more
detail for the parsing of the affect elements:
Inspired by local grammar
Inspired by FrameNet
description of one area of meaning (a include non-patterns
unified description)
functional orientation
include patterns of items that co-occur with
emotion term
meaning groups
include some collocates that are part of specific (and important) patterns, e.g. expressor
Along the lines of suggestions by Hunston (2003: 16), the functional local grammar approach was used to ‘tidy up’ and ‘organize’ the details emerging from FrameNet and
corpus analyses, and some non-patterns were labelled as local grammar elements (e.g. V
n in n: We admire this characteristic in others, BNC, B19 1079), whereas phrases that
are part of noun phrases/adjective phrases (e.g. Rory hated his mother in a hat, BRC,
G0A 829) were not listed, e.g. as V n in n (though sometimes the difference may be
difficult to capture). The resulting analysis improved the delicacy of the pattern
approach, which ‘given the impressive scale of this work, […] is often rather coarse’
(Stubbs 2001: 460), and was more concise than the FrameNet approach, which can be
overwhelming in its detail.
6
In order to investigate this phenomenon further I analyzed the right-hand noun collocates
for four emotion adjectives in detail: anxious, disappointed, delighted and surprised. Of
these four adjectives, three occur only with nouns that indicate an emoter. This noun is
either a conscious participant (or metonymically an institution, a collective), an expressor, or a linguistic/mental activity:
Disappointed (excluding disappointed expectations)
•
noun = conscious participant (e.g. birdwatcher, competitors, English graduate, ghost,
man, parents, pigeons, NAME, cleric; clubs (metonymic))
•
noun = expressor (e.g. eyes)
•
noun = linguistic/mental phenomenon (e.g. editorial, huffing and puffing, rage)
26
Delighted
•
noun = conscious participant (e.g. grandfather, NAME, boy, fan, parents, Republicans, workers)
•
noun = expressor (e.g. cackle, cascade [of laughter], countenance, face, kisses, laugh,
laughter, squeal, whoop, kisses)
•
noun = linguistic/mental phenomenon (e.g. anticipation, perception, disbelief, outrage, wonder)
Surprised
•
noun = conscious participant (e.g. beast, NAME, servant, young men, bosses, patients, Toronto fan)
•
noun = expressor (e.g. expression, look, fingers, voice)
•
noun = linguistic/mental phenomenon (e.g. approval, reply)
(Surprised also occurs with way modifying an action by an emoter: The surprised way
he had looked at her, BRC, CEC 2373)
The parsing of such adjectives does not entail any major problems: all ADJ n patterns can
be parsed as undirected affect (with some perhaps additionally involving appreciations of
semiotic phenomena).
With anxious, the situation is more complex (even disregarding its use in sexual descriptions: anxious + ‘genital’ as well as one instance of an anxious blue blur in BRC, FRL
1397).
When the right-hand noun collocate points to an emoter, the noun is either a participant,
an expressor or a linguistic/mental phenomenon as with the other three adjectives, or (in
addition) a noun referring to an activity that is clearly related to an actor:
•
noun = conscious participant (e.g. anxious children, clients, individual, patient, people, person, lovers, owners, shop stewards, worker, test takers, wife etc, maybe also
anxious presence)
•
noun = expressor (e.g. expression, eye(s), face(s), features, neck, tone, voice) à
showing the emoter’s anxiety
•
noun = linguistic/mental phenomenon (e.g. questions, reflection, consultations,
discussion, fussing, moan, quaking, account, debate, inquiry, reception, silence,
attention, attachment, spiritual commitment, [mental] state, an anxious shrinking
from everything except duty) à pointing to the emoter’s (sayer’s/senser’s) anxiety
27
•
noun = activity related to an actor (e.g. galvanising the Gnomes into anxious industry, BRC, G1L 79; She put her hands on the bedspread, looked into his eyes, an
anxious searching, BRC, H7F 1228; She took an anxious step towards him, BRC,
BMW 389) à pointing to the emoter’s (actor’s) anxiety
But anxious can also occur with nouns that point to triggers (rather than or in addition to
emoters). One example was already mentioned above:
(F1)
OLDHAM keeper Jon Hallworth is facing an anxious battle to be fit in time for
Saturday’s big Premier League kick-off. (BRC, CH7 869)
In this example, it is presumably the ‘battle to be fit in time’ that causes Jon Hallworth’s
anxiety but the noun also metonymically allows us to infer the emoter (as the one facing
the battle). Anxious frequently occurs with nouns indicating a duration of time, too
(moment(s), days, plane journey, day(s), week(s)). For example:
(F2)
But Roy had thrown away his script, and he spent an anxious plane journey
trying to remember his lines. (BRC, CH1 3108)
(F3)
The 26- year-old midfielder, who played in all three of England’s European
championship games, now faces an anxious wait for Taylor’s verdict. (BRC,
CH7 2804)
(F4)
JONATHAN SPEELMAN gave his supporters an anxious day in the sixth
round of the Pilkington Glass World Chess Championship semi- finals
yesterday. (BRC, A4K 729)
What these examples are saying is that these times (moments, days etc) are spent by
emoters in a state of anxiety because of something that happens during these times. It is
not the days themselves that cause the anxiety, but the happening during these days. For
example, that Roy has thrown away his script causes his anxiety (rather than the plane
journey) or the worry about what Taylor’s verdict might be or Jonathan Speelman’s chess
play in the semi- finals on the day in question. That is, such nouns can neither be parsed
as emoter nor as trigger. Even more complex is an adjective like sad, which can express
both emotion (‘being unhappy’, ‘showing unhappiness of emoters’) with collocating
nouns as emoters or ‘making someone feel unhappy’ with collocating nouns as triggers)
and opinion (‘‘unacceptable, deserving blame’, ‘boring, not fashionable’, ‘in poor condi28
tion’). Collocating nouns cannot easily be used to distinguish these meanings without the
wider context. Thus, a sad man might be unhappy, or ‘boring, not fashionable’ and a sad
consequence can arguably either indicate affect (‘making you unhappy’) or opinion
(‘unacceptable, deserving blame’). The collocates for sad would need to be examined in
detail to find out how accurate they predict the meaning of sad.
7
This points to the fact that conversation is similar in one sense to both news reportage
and fiction (e.g. in containing a certain amount of narrative), while being different in another sense from all written discourse, e.g. in being spoken and interactive (Biber et al
1999: 16). Academic discourse, on the other hand, is sometimes similar to news reportage and fiction (because like them, it is a written register), while also differing from
conversation, news reportage and fiction in that it presumably does not contain as much
narrative and hardly any dialogue.
8
These findings might change if the analysis of patterns is broadened to positions other
than L1 and R1 and to patterns that are less frequent in each register (compare Bednarek
(2008: Chapter 4) for the methodology of the analysis of lexico- grammatical patterning).
9
More detail would also be helpful where emotion adverbs are concerned, which are often
only run-on entries in dictionaries on account of their infrequency, though they have a
variety of different meanings (see Appendix 2 for some of the meanings of desperately,
happily, cheerfully).
29
Appendix to Chapter 6 of Emotion Talk Across Corpora (Appendix
6)
Contents:
A 6.1 Description of corpus (BRC baby)
2
A 6.2 Description of methodology
11
A 6.3 Affect sub-types in conversation sub-corpus of the BRC baby
13
A 6.4 Applications and implications
14
A 6.1
Description of corpus (BRC baby)
The BRC baby consists of about 20,000 words from the registers of conversation, news reportage, fiction and academic discourse. Sampling procedures are described in Bednarek
(2008: Chapter 5). More detailed information (including titles of books, type of sample, circulation size, level of difficulty, author gender, speaker age and occupation etc) is listed below:
Conversation (22,613 words)
Samples from 7 files:
KBC, part 1, 1-337
KBD, part 6, 1916-2243
KBP, part 5, 1233-2004
KD0, part 3, 125-346
KD8, part 15, 7154-7413
KE2, part 21, 3027-3281
KE4, part 2, 102-491
Further information on files from which samples were chosen:
KBC:
3019 words (AB) from 14 conversations recorded by ‘Audrey’ (PS1A9) between 2 and 9
April 1992 with 9 interlocutors, totalling 6341 s-units, 31,337 words, and 3 hours 38 minutes
41 seconds of recordings.
Speakers:
PS1A9 Audrey, 61, housewife, Lancashire
PS1AA Gordon, 61, teacher, Lancashire
PS1AB Margaret, 45, nurse, Lancashire
PS1AC Joan, 50+, clerk, Central Northern England
PS1AD Kevin, 29, computer engineer, Northern
England
2
PS1AE Carl, 31, pharmacist, Northern England
PS1AF None ? ? ?
PS1AG Elaine, 28, housewife, Northern England
PS1AH Iris, 60, housewife, Lancashire
KBD
2690 words (AB) from 24 conversations recorded by ‘Barry’ (PS03W) between 1 and 6 February 1992 with 10 interlocutors, totalling 9021 s- units, 58,087 words, and 5 hours 12 minutes
10 seconds of recordings.
Speakers:
PS03W Barry, 41, entertainments consultant, Central Northern
England
PS03X
Terri, 35, bar staff, Home Counties
PS03Y
Hugh, 30, bar staff, Irish
PS040
Alan, 38, security, Lancashire
PS041
None ? ? ?
PS042
Mark, 30, dj, London
PS043
Ken, 30, security, Lancashire
PS044
None, 35, housewife, Lancashire
PS045
Sergio, 9, student (state primary), Lancashire
KE 4:
2683 words (C1) from 22 conversations recorded by ‘Valerie’ (PS0WN) between 30 January
and ?? ?? 1992 with 7 interlocutors, totalling 3280 s- units, 15,170 words, and 1 hour 55 minutes 21 seconds of recordings.
Speakers:
PS0WN Valerie, 36, staff nurse (pt), Scottish
PS0WP Peter, 34, sales representative, Scottish
PS0WR Jackie, 8, student (state primary), Scottish
3
PS0WS David, 10, student (state primary), Scottish
PS0WT Dawn, 11, student, Scottish
PS0WU None, 40+, dentist, Scottish
PS0WW None, 50+, telephone engineer, Scottish
PS0WX Dougie, 37, sales representative, Scottish
KD0:
2951 words (C1) from 106 conversations recorded by ‘Kevin’ (PS0HM) between 29 November and 5 December 1991 with 14 interlocutors, totalling 13,948 s- units, 77,692 words, and 10
hours 39 minutes 22 seconds of recordings.
Speakers:
PS0HM Kevin, 41, draughtsman, London
PS0HN Paul, 12, student (state secondary), London
PS0HP Ruth, 40, teacher, ?
PS0HR Michelle, 29, local government officer, European (French)
PS0HS Eric, 74, retired, London
PS0HT Adrian, 40, salesman, London
PS0HU Karen, 41, secretary, London
PS0HV Andrew, 33, local government officer, London
PS0HW Lisa, 13, student, London
PS0HX Babs (aka mutty), 70+, retired, Lower South-west England
PS0HY Joy, 70+, retired, London
PS0J0
Michael, 15, student, London
PS1KN None ? ? ?
KE 2:
2623 words (C2) from 153 conversations recorded by ‘Terence’ (PS0W2) between 20 and 27
February 1992 with 10 interlocutors, totalling 10,080 s-units, 77,961 words, and over 12
hours 49 minutes 22 seconds of recordings.
4
Speakers:
PS0W2 Terence, 70, retired (headteacher), East Anglia
PS0W3 Richard, 44, fireman, Lower South-west England
PS0W4 Margaret, 70, retired, Irish
PS0W5 Lucy, 13, student, Lower South-west England
PS0W6 Holly, 13, student, Lower South-west England
PS0W7 Adrian, 13, student, Lower South-west England
PS0W8 Danielle, 13, student, Lower South-west England
PS0W9 Christine, 40, housewife, Lower South-west England
PS0WA Mima, 50, housewife, Lower South-west England
KD8:
2654 words (C2) from 31 conversations recorded by ‘Martine’ (PS0LK) between 12 and 20
March 1992 with 10 interlocutors, totalling 10,787 s-units, 76,445 words, and over 7 hours 15
minutes 1 second of recordings.
Speakers:
PS0LK
Martine, 25, senior technician, Welsh
PS0LL
Mike, 28, construction worker, Welsh
PS0LM Merielle, 55, housewife, Welsh
PS0LN
None, 45, pub landlord, Home Counties
PS0LP
Harold, 58, engineer, Welsh
PS0LR
Nora, 76, housewife, Welsh
PS0LS
Will, 45, civil engineer, Merseyside
PS0LT
Michael, 40, technical director, Home Counties
PS0LU
Jim, 27, technician, Home Counties
KBP:
5993 words (DE) from 15 conversations recorded by ‘Clarence’ (PS065) between 13 and 19
March 1992 with 4 interlocutors, totalling 5039 s- units, 27,179 words, and 2 hours 23 minutes
42 seconds of recordings.
5
Speakers:
PS065 Clarence, 65, retired, Lancashire
PS066 Nina, 67, retired, Lancashire
PS067 Nev, 72, retired, North-east Midlands
PS068 Lil, 70, retired, Lancashire
News reportage (18,164 words)
30 articles from seven files (no tabloids):
A1E 1-145
A1G 1-158
A1M 1-138
A1N 1-160
A7 S 1-159
AL5 (all)
AJG 1 (all)
AL5 and AJG (Social): 3004 words
(1) ‘Struggling dentists’ pull more teeth
(2) Misconduct case GP to appeal
(3) ‘The NHS is not for sale’
(4) Marital strife comes out in the wash
(5) CALLED TO ORDER
(6) Sex education for priests urged by Pope
(7) Homosexual prayer book to go on sale
A7S (Arts): 3003 words
(8) Forget Kylie — here come the crisp bags
(9) What’s going on
(10) Jackboots beneath the serge
(11) OPERA: Edward Greenfield finds the Medea at Covent Garden relentlessly
loud Cool, but not Callas enough.
(12) For third South arts QEH
6
A1N (Sports): 3058 words
(13) Cricket First Test: Azharuddin’s daring defiance
(14) Motor Racing: Fighting Senna refuses to succumb
(15) Golf: Calcavecchia digs in for consolation prize
(16) Rugby Union: The dye is cast for injured Richards
(17) Football: FA investigates crowd trouble
A1M (science): 2747 words
(18) The revolution the countryside needs: Christian Wolmar says Britain is lagging behind in setting up ‘telecottages’
(19) The busy sex life of the nice male
(20) Cool solutions for hot climates: David Spark looks at tropical temperature
controls for vaccines
A1G (report): 3145 words
(21) Tigrayans advance on a helpless Addis Ababa
(22) Tribalism and liberation meet at Transkei burial
(23) Swapo remains favourite to win the United Nations-supervised elections next
month
(24) Out of India: The snow-wreaths melt away in the heat of battle
(25) Russians send Kabul 2000 supply trucks
A1E (Commerce): 3207 words
(26) Latest corporate unbundler reveals laid-back approach: Roland Franklin, who
is leading a 697m pound break-up bid for DRG, talks to Frank Kane
(27) Square Mile: Stock Exchange takes the bull by the horns
(28) View from Manhattan: Airlines fly into regulatory storm
(29) USM: Remedial action at Lincat has results
(30) MB and Caradon stay quiet on bid rumours
Fiction (20,563 words)
Beginning samples from 10 files:
7
AB9 1-145
AC2 1-105
BMW 1-133
C8T 1-100
CB5 1-144
CFY 1-152
FAJ 1-142
G0S 1-101
H9C 2-168
HR9 1-175
AB9 (2058 words)
Death of a Partner, Neel, Janet, Constable Company Ltd, London (1991)
= beginning sample, medium circulation size, female author, medium level of difficulty
AC2 (2159 words)
Man at the Sharp End, Kilby, M, The Book Guild Ltd, Lewes, East Sussex (1991)
= end sample, medium circulation size, male author, medium level of difficulty
BMW (1998 words)
Folly’s Child, Tanner, Janet, Century Hutchinson, London (1991)
= beginning sample, high circulation size, female author, medium level of difficulty
C8T (2222 words)
Devices and Desires, James, P.D., Faber Ltd, London (1989)
= beginning sample, high circulation size, female author, medium level of difficulty
CB5 (2048 words)
Ruth Appleby, Rhodes, Elvi, Corgi Books, London (1992)
= middle sample, high circulation size, female author, medium level of difficulty
CFY (1999 words)
My Beloved Son, Cookson, C., Corgi Books, London (1992)
= middle sample, high circulation size, female author, medium level of difficulty
8
FAJ (2047 words)
Masai Dreaming, Cartwright, J., Macmillan Publishers Ltd, Basingstoke (1993)
= middle sample, medium circulation size, male author, high level of difficulty
G0S (2111 words)
Indigo, Warner, Marina, Chatto Windus Ltd, London (1992)
= middle sample, medium circulation size, female author, high level of difficulty
H9C (1933 words)
The Prince of Darkness, Doherty, P.C., Headline Book Publishing plc, London (1992)
= middle sample, medium circulation size, male author, medium level of difficulty
HR9 (1988 words)
They Came from SW19, Williams, Nigel, Faber Ltd, London (1992)
= end sample, high circulation size, male author, medium level of difficulty
Academic discourse (23,781 words)
Beginning samples from 10 files:
A6U 1-109
ACJ 1-101
ALP 1-101
AS6 1-80
EA7 1-93
EWW 40-139
FC1 1-83
FEF 1-121
FPG 1-101
HWV 1-93
A6U: 2411 words from:
‘Being Drawn to an Image’, Guy Brett, Oxford Art Journal (1991)
sample type unknown, from periodical, multiple authors, high difficulty
9
ACJ: 2666 words from:
Principles of Criminal Law, Andrew Ashworth, OUP, Oxford (1991)
= middle sample, from book, male author, high difficulty
ALP: 2285 words from:
‘A Non-punitive Paradigm of Probation Practice: Some Sobering Thoughts’, L.R. Singer,
British Journal of Social Work (1991)
= middle sample, from periodical, multiple authors, high difficulty
AS6: 2073 words from:
Tackling the Inner Cities, Ben Pimlott and Susanne MacGregor, OUP, Oxford (1991)
= beginning sample, from book, multiple authors, high difficulty
EA7: 2511 words from
France in the Making, 843-1180, Jean Dunbabin, OUP, Oxford (1991)
= middle sample, from book, female author, medium difficulty
EWW (without foreword: tribute): 2299 words from:
Matrices and Engineering Dynamics, A. Simpson and A.R. Collar, Ellis Horwood Ltd,
Chichester (1987)
= beginning sample, from book, multiple authors, medium difficulty
FC1: 2333 words from:
‘In re A DEBTOR (NO. 784 OF 1991) 1992 April 13’, J. Hoffmann, The Weekly Law
Reports, vol 3 (1991)
= sample type unknown, from periodical, author details unknown, high difficulty
FEF: 2124 words from:
Lectures on Electromagnetic Theory, L. Solymar, OUP, Oxford (1984)
= beginning sample, from book, male author, high difficulty
FPG: 2293 words from:
Design of Computer Data Files, O. Hanson, Pitman Publishing, London (1989)
= middle sample, from book, male author, high difficulty
10
HWV: 2786 words from:
‘Immunogenicity of a Supplemental Dose of Oral Versus Inactivated Poliovirus Vaccine’
The Lancet, London (1993)
= unknown sample type, from periodical, multiple authors, medium difficulty
A 6.2
Description of methodology
The data was analyzed and coded with the help of Altova XMLSpy 2007 (www.altova.com),
an XML editor software, which allows you to tag data with a number of attributes (Bednarek
2008: Chapter 5). Each emotion term was coded on nine linguistic variables:
1) Affect type
2) Affect trigger
3) Covert1 -overt affect
4) Emoter
5) Hypotheticality
6) Negation
7) Part of speech2
8) Valence
9) Speech act
Remarks on affect type, covert and overt affect and valence are made in Bednarek (2008:
Chapter 5), so that this section focuses on hypotheticality, negation, and speech act, with the
analysis of the remaining variables being relatively straight- forward and in no need of further
elaboration.
Hypotheticality
Under the heading of hypotheticality the analysis focused on whether an emotion was described as being experienced (in the past or present) in reality, or whether its experience was
predicted (in the future) or just hypothesized (in a possible world). Table A.21 shows typical
analyses of emotions as ‘hypothetical’, ‘will’ (future) or ‘real’:
11
Table A.21: Analysis of hypotheticality
Coding
Typical analyses
‘Hypothetical’
deontic and dynamic modality when referring to non-real, non-actualized emotion;
hypotheticality; intention etc: would/’d, could (past + future), should, can, (in order)
to, the purpose was to, have to, must be, might, in any desired order, ought to,
impossible to, if, as if, as often as she wished, had been about to, unless, wanting
someone to feel, for (‘in order to’), try to look like a man who enjoyed, designed to,
test whether, whether or not x is around to be impressed, the opportunity to, would
work for, I want to see…, inclinations towards, prevent, whatever you want
won’t, shall, ‘ll, will, … ahead
everything else, including evidentiality and epistemic modality, reported emotions,
e.g.: seem, evidence of, I bet, on the face of it, predict, obviously, I understand (that),
a message that, you don’t say you love, accuse of, perhaps, I thought you liked it,
apparently etc3
‘Will’
‘Real’
Negation
The categorization of negation relates to whether or not an emotion is negated. As noted in
Bednarek (2008: Section 5.3.2.2), there is a distinction in appraisal theory between negative
emotions (sad) and negated positive emotions (not happy), with the latter coded as ‘neg +
hap’ rather than ‘-hap’. Thus, ‘negated’ relates to grammatical negation (not, no, never etc)
whereas ‘non- negated’ includes morphological (negative prefix (un-, in-, dis-) or suffix
(-less)), and lexical negation (such as disinclined, refuse, reluctant, dislike, doubtfully). On
emotion words and negation see also Nöth (1992).
Speech act
Finally, the category that I have labelled ‘speech act’ here purely relates to whether the emotion is questioned (e.g. do you want x, do you love me?) or asserted (e.g. I am furious). This
does not in fact correspond to whether the speech act as such is a question or not; for instance
examples such as Why does it interest you? What do you want? are questions but contain asserted emotions (in contrast to Does it interest you? Does she want to collect you?). Only if
the experience of the emotion is questioned to some extent was this coded as ‘question’ rather
than ‘statement’ (tag questions were not counted as questions either, relating to mitigation/hedging). ‘Question’ also includes reported questions, for example consider whether they
really wanted to.
12
A 6.3 Affect sub-types in the conversation sub-corpus of the BRC baby
120
Misery
100
Non-desire
Displeasure
80
Interest
Antipathy
60
Surprise
Cheer
40
Pleasure
Disquiet
20
Affection
0
Desire
KBC
KBD
KBP
KD0
KD8
KE2
KE4
Desire Affection Disquiet Pleasure Cheer Surprise Antipathy Interest Displeasure Non-desire Misery
KBC
44.4
16.6
11.1
11.1
5.5
11.1
0
0
0
0
0
KBD
15.4
57.7
0
3.8
0
0
7.7
7.7
7.7
0
0
KBP
37.5
20.8
0
4.2
4.2
12.5
4.2
0
12.5
4.2
0
KD0
41.2
11.8
35.3
0
0
0
0
0
11.8
0
0
KD8
47.1
17.6
11.8
0
0
0
11.8
0
5.9
0
5.9
KE2
64.7
35.3
0
0
0
0
0
0
0
0
0
KE4
64.3
21.4
0
7.1
0
0
7.1
0
0
0
0
(Figures refer to percentages of affect sub-type with respect to all emotion terms in given file; for instance,
44.4% of all emotion terms in KBC realize Desire)
A 6.4 Applications and implications
In Bednarek (2008: Chapter 6) I noted that the findings might have some implications for:
•
the application of appraisal theory;
•
the modelling of probabilistic (intra- and inter-) register variation;
•
natural language processing (automated register recognition, parsing of affect);
•
language teaching and lexicography.
The following sections provide a more detailed discussion of these aspects.
13
Appraisal theory
In terms of appraisal theory, there is a need for discourse analysts to try out the new classificatory system of affect (including the nine variables identified above), in order to test its
applicability, its advantages and disadvantages. More (theoretical) research is also called for
in the areas of:
•
Surprise and counter-expectation: should this be established as an evaluative (appraisal) system in its own right?
•
Types of covert affect: what is the usage and patterning of, and difference between
nouns such as worries, disappointments, adjectives such as worrying, and adverbs
such as sadly? Are there also verbs that indicate covert affect?
•
Authorial vs. non-authorial appraisal: does non-authoria l appraisal involve intersubjectivity (engagement)?4
•
Appraisal and grammatical metaphor: when do appraisal expressions construe modality, when affect/engagement (see Martin 1992, 2000b, Martin & White 2005: 54-56)?
•
What is the role of talk about not experiencing an emotion, rather than talk about
experiencing an emotion? Galasinski notes that in his data there is a ‘narrative awareness of the fact that certain events in people’s lives […] might be associated with certain emotional experiences and the y explicitly acknowledge this by denying ha ving
these experiences.’ (Galasinski 2004: 83). How is this related to appraisal? It might
also be interesting to examine talk about hypothetical emotions (Galasinski 2004: 84).
Since the BRC baby was coded for hypotheticality (see above), this aspect can easily
be investigated in the future.
•
Affect and intensity (graduation): how are degrees of intensity conveyed in emotion
talk? This is likely to be a huge research project: ‘Studying language intensity in emotion talk will be challenging because modifiers of emotion terms, inflectional and intonational changes, and even exclamations will likely need to be considered’ (Anderson & Leaper 1998: 443). For already existing studies on intensity/involvement see
Bednarek (2008: Section 1.3).
•
Appraisal and polyphony (Downes 2000): what is the interplay between different
evaluative (appraisal) systems and can a typological description capture this?
•
Appraisal and textual structure: how do the prosodic structure of interpersonal meaning, and the periodic structure of textual meaning interact? How is evaluative meaning
14
distributed in texts (elaborating on research by Martin 1992, 1997, 2002a, 2004,
Macken-Horarik 2003: 317, Martin & White 2005: 85-89)?
Current research addresses these issues in more detail (e.g. Bednarek 2007). It must also be
pointed out that the focus of Bednarek (2008) was on emotion talk (the use of emotion terms)
rather than emotional talk (compare Bednarek 2008: Chapter 1), though there is a whole range
of resources for emotional talk without the use of emotion terms: ‘An explicit emotion
vocabulary is not necessary for powerful displays of emotion with language in its full
pragmatic environment’ (Goodwin & Goodwin 2000: 254).
Modelling register variation
Findings about frequencies in text are useful for a variety of reasons, providing us with information about the semiotic system itself (Halliday 2005: 45) and the modelling of register
variation. The establishment of probability profiles (in Bednarek 2008: emotion profiles) has
implications for ‘at least five areas of theoretical enquiry: developmental, diatypic, systemic,
historical and metatheoretic.’ (Halliday 2005: 73). Knowing about the frequency of words is
also important in various areas of language teaching, for example the design of curricula, the
writing of materials and the testing of language proficiency (Leech et al 2001: ix), and in the
fields of natural language processing, linguistics, psychology, and cultural studies (Leech et al
2001: x). Leech et al also point out that
for the various uses of frequency information mentioned earlier, particularly in the
educational arena, we need to reckon on different frequency profiles for different varieties of the language. The idea that one monolithic frequency list for the whole
language can satisfy all needs is, of course, unrealistic. (Leech et al 2001: xi).
As we have seen in Bednarek (2008), there is some variation in the emotional profiles of the four
registers investigated here. If we want to include such register variation in a description of affect,
how can we model it? SFL lends itself quite well to probabilistic modelling, since it recognizes
that there are probabilistic tendencies in language, and recently several studies have addressed the
issue of modelling co-selection or intersections of systems (e.g. Matthiessen 2006, Tucker 2006).
It is suggested that system networks can be used to represent probabilistic tendencies:
15
the system network can represent not only the dependency of one system on another
or others, but also any probabilistic correlation between any two features anywhere
in the network. We are thus able to represent the relationship between choices in the
various systems, such as transitivity, tense, mood etc., which are manifested through
syntagmatic co-occurrence. […] Importantly, the notion of pre-selection is introduced into the grammar. The selection of any feature, or combination of features,
may lead to the pre-selection of subsequent features, either in terms of absolute preselection or of the setting of probabilities on the features in the system(s) in question. (Tucker 2006: 92; emphasis in original).
The modelling of probabilistic variation is arguably an integral part of any description of the
lexico-grammatical resources of a language (Matthiessen 2006). Additionally, it may provide
input for any future computational linguistic modelling. Even though networks are specifically
used in SFL, they also represent a user-friendly device to taxonomize probabilistic tendencies of
corpus-linguistic (CL) evidence regardless of the theory researchers adhere to. For examples of
probabilistic modelling in SFL see Tucker (2006), and Matthiessen (2006). For a discussion of
SFL vs. CL principles see Hunston & Thompson (2006), especially Hunston (2006). However,
more research is necessary on intra-register variation (see Bednarek 2008: Chapter 6 for
preliminary comments). The crucial question is whether there are more similarities between texts
across registers than between texts within a certain register. This should be determined with the
help of sophisticated statistical measures (Biber 1989, Kilgarriff 2001).
Natural language processing
Automated register recognition
Can the findings about the frequency of emotion terms in different registers help automated
register recognition? A good overview of previous research and criteria on differentiating
between text types is given by Stubbs & Barth (2003: 79). It seems likely that in as far as
emotion terms are content words, they are at least partly dependent on topic, and ‘may
therefore be frequent in an individual text, but absent in another text from the same text-type.’
(Stubbs & Barth 2003: 67). Further, the kind of analysis that I have undertaken was meaningsensitive, and cannot yet be automated. More promising are word-chains (Stubbs & Barth
2003: 62) or lexico- grammatical patterns/local grammars as described in Chapters 3 and 4.
16
Parsing affect
As Hunston & Sinclair propose, parsers can be developed on the basis of local grammars that
can automatically extract information from texts (e.g. the automatic retrieval of definitions
from texts) (Hunston & Sinclair 2000: 82). A parser that could be developed on the basis of
the description in Bednarek (2008: Chapter 3),5 i.e. that is programmed with patterns that
allow it to automatically produce a simple table or list of emoters, emotions and triggers from
texts, might be useful enough (though it is important that negation be included to capture the
distinction between love and no/not love – but this is easy when pre-processed corpora are
used). Although the relations between emoter, emotion and trigger may vary according to the
particular emo tion involved, the human analyst can decide what these relations are, on the
basis of the out put of the parser and his/her linguistic and non-linguistic knowledge. If the
parser output is emotion = love, trigger = you, the relation is one of direction; whereas if the
parser output is trigger = the results, emotion = surprise, the relation is one of cause.
Ultimately, however, more detail (though not all) that can be found in FrameNet could be
added to parsers. For example, if the degree element could automatically be parsed by the
software, it would be possible to produce a list of emoters, emotions, triggers and the degree
of emotion involved. In terms of appraisal theory, the results of this parsing would show both
affect and graduation – two of the sub-systems of appraisal (Bednarek 2008: Section 1.4).
More and more details could gradually be included (from FrameNet and other corpus research) to make the parser more and more sophisticated (to enable it to deal with variations of
patterns, e.g. pseudo-clefts, changed word-order and so on. Compare Francis et al 1996: 611615).
Since I am neither a computational linguist nor an AI researcher, I cannot authoritatively
discuss how easy or difficult the development of such a parser could be (for a discussion of
some issues regarding a local grammar of evaluative adjectives see Hunston & Sinclair 2000:
82, and Hunston 2002: 180). However, the following factors could cause some problems for
such an application:
•
Patterns come in different forms and are changed by processes such as clefting, fronting, passivization etc (see Francis et al 1996: 611-615, Hunston & Francis 2000).
•
The difference between undirected and directed affect patterns is superficial: triggers
of presumably undirected affect patterns may be explicitly stated in the context or inferable by readers/hearers.
17
•
Patterns can have different mappings depending on the lexical item or meaning group
involved (see also Hunston 2003: 7, Hunston & Sinclair 2000: 88), e.g.:
(n) V n:
(i) emoter emotion trigger (I admired them like I admire Tom Wolfe, BNC, CHA
2382 )
(ii) trigger emotion emoter (Her reaction surprised me, BNC, H0D 2326 )
ADJ for n:
(i) emotion for trigger (Regan, anxious for an alternative theoretical platform from
which to put into orbit his conviction, BRC, CM8 491; enthusiastic for young
athletes to do well, BRC, CH6 370; desperate for money, BRC C8E 198, anxious
for her sons, BRC, CCD 2410; willing for more fun, BRC, FPF 2661)
(ii) emotion for empathy target (I am happy for him, BNC, H8G 790; I am delighted
for you, BRC, AE0 2465; I’m pleased for you, BRC, KCX 644; I’m frightened for
him, BRC, CH3 3192; I am very disappointed for Jimmy, BNC, AHC 662).
ADJ n
(i) emotion emoter (anxious faces, BRC, H8A 1451)
(ii) emotion trigger (sad sound, BNC, G0Y 2062)
Thus, the order of the elements emoter, emotion, and trigger varies with directed affect
patterns (depending on the verb), and the pattern ADJ for can express both directed
(with trigger) and undirected affect (with empathy target). In such cases, the parser has
to be given lists of lexical items that occur with each mapping. With the pattern ADJ
n, the matter is more complex, since it seems to depend on the noun whether directed
or undirected affect is concerned. Research would be necessary to determine which
kinds of nouns typically function as emoters and which as triggers.6
•
The local grammar description involves patterns (e.g. passives PV by n, basic patterns
such as ADJ n) that also occur in areas outside affect:
PV by n
He … was respected and admired by all of his colleagues in the forces (FrN) (affect)
PV by n
After their divorce Jane worked on, helped by students at the growing research
station. (BNC, A7D 448)
ADJ n
They were admitted by a surprised servant (BRC, CD2 1395) (affect)
ADJ n
The old man and the girl are listening attentively. (BNC, A04 1362)
Again, the parser would have to be given lists of lexical items inscribing affect to distinguish such patterns.
18
•
In some cases, it depends on the context whether when-clauses and because-clauses
can be mapped as trigger or not. In examples 1-2 below the clauses seem to express
circumstances of time with respect to an emotion directed by an emoter at a trigger,
rather than triggers of emotions themselves (as in examples 3-5):
(1)
When she was quite small [circumstance] she had been surpris ed [emotion] to see the woman
carrying a folded table and a Gladstone bag [trigger]. (BRC, AEA 176)
(2)
When he looked up [circumstance], he seemed surprised [emotion] to find Lacuna still staring
at him [trigger]. (BRC, F9X 3448)
(3)
When presently the young person came in [trigger], he was definitely surprised [emotion].
(4)
She was asked to study, so she did, and seemed surprised [emotion] when her application
produced good marks [trigger]. (BRC, HWE 2039)
(5)
He looked even more surprised [emotion] when Elaine walked into the lounge with two men
[trigger]. (BRC, FAB 2451)
Similarly, because-clauses can either refer to the triggers of an emotion (you are disappointed because something happened), as in examples 6-7 or they can be used to
elaborate why someone feels disappointed at a trigger (examples 8-9):
(6)
I’m really disappointed [emotion] cos I don’t think my mum will let me stay now! [trigger]
(BRC, KCE 251)
(7)
I was really disappointed [emotion] cos erm I didn’t get that one from Mencap [trigger]
(BRC, KCP 5756)
(8)
[…] last time on a, I was really disappointed [emotion] I’d been refused the course [trigger] cos
I thought oh, it’ll be right up my street that (BRC, KBW 15192)
(9)
In fact, we’re disappointed [emotion] with it [trigger] cos usually we have vegetables out of
our own garden all the year round (BRC, KC0 5317)
Since it seems that when- and because-clauses are only triggers when no other triggers
are identifiable in the sentence (examples 3-7), the parser must be trained to include
when- and because-clauses as triggers only if no other triggers have already been
parsed.
However, if a way can be found of solving these (and perhaps other) problems (maybe
through combining a thesaurus-based seed list of emotion terms with information on patterns),
a parser could be developed that automatically retrieves inscribed affect from texts, listing
emoters, emotions and triggers. As John Patrick (p.c.) has noted, sentiment classification is of
19
growing interest in computational linguistics, with an increase in publications about detecting
and classifying sentiment and subjectivity in discourse (compare also Taboada & Grieve
2004, Whitelaw et al 2005, Bloom et al 2006), and many important conferences have been
held on this topic. The analysis of appraisal has a wide-range of applications, for example
‘data and web mining, market research, and customer relationship management’ (Whitelaw et
al 2005: 1). Some of these approaches already use appraisal theory and include different
appraisal attributes such as attitude type, orientation, force, polarity and target type (Taboada
& Grieve 2004, Whitelaw et al 2005, Bloom et al 2006, focussing on adjectives).
Language teaching and lexicography
Language teaching
Pattern-based descriptions of local grammars are also potentially interesting for language
teaching purposes. Patterns, as Hunston (2002: 173-174) notes, can be taught to students to
improve both fluency and accuracy. The particular advantage of a local grammar approach to
patterns lies on the one hand in their transparent, very ‘user- friendly’ category labels, i.e. semantic labels such as emoter, emotion, and trigger that directly reflect the discourse function
of sentence elements (which the traditional categories of subject, verb etc do not). On the
other hand, the organization of local grammars according to meaning means that exercises can
be developed that cover a particular area of meaning – one that might be important to the particular students involved. Hunston (2002) discusses some examples of teaching methods
appropriate to pattern teaching (without a specific discussion of local grammars).
Lexicography
A number of questions for lexicography seem to arise from the findings outlined in Bednarek
(2008). The first concerns the variability of lexico-grammatical patterns investigated in Bednarek (2008: Chapter 4). While some of these terms show a very strong preference for interregister stability, others demonstrate inter-register variation. In other words, with some
20
terms there is not much variation in terms of the most common L1 or R1 collocate, whereas
others do vary across registers.
Table A.22 below sums up the most frequent L1 collocates of the analyzed emotion terms in
the four corpora. As we can see, enthusiastic, hate (V) and delighted (and perhaps also
admire, surprised, and surprise (V)) exhibit relatively stable L1 collocates across at least
three corpora (red), whereas other emotion terms are more varied (e.g. surprise (N), anxious,
disappointed, impressed, pleased, willing, affection, frightened). That is, we can make a
distinction between lexico-grammatically stable (LG-stable) and lexico- grammatically
volatile (LG-volatile) emotion terms.
Table A.22 L1 collocates of emotion terms across corpora
L1
A
C
N
F
surprised
be
‘m, (not)
be, (was)
was, (be)
surprise (N)
no
a
a
in
surprise (V)
(not)
n’t, (it)
not
to
admire (V)
to
NN (I, and)
I, (to)
to
anxious (A)
was
NN (was)
is
was
disappointed
be
(a) bit
be, very
was
enthusiastic
an
NN (very)
an
an
impressed
be
very
so
was
pleased
be
very
very
was
willing
be
are/re
be
was
affection
and
NN (had, no)
with, and, ‘s
of, with
hate (V)
NN (I)
I
I, (he)
I, (she, he)
delighted
NN (be)
was
was
was
frightened
NN (too, BECOME) was
are
was
hate (N)
NN
NN
of
NN
Considering only LG-volatile terms, news reportage shares collocates with conversation (a
surprise, very pleased) (pink), with academic discourse (be disappointed, be willing, and
affection) (green) and fiction (with affection) (blue). Conversation shares two L1 collocates
with news reportage (as mentioned), and one with fiction (was frightened) (grey), but none
with academic discourse. Fiction shares collocates with news reportage and conversation (as
noted), and with academic discourse (was anxious) (yellow).
Moving on to R1 collocation, Table A.23 (on the next page) sums up their distribution across
the four corpora.
21
Table A.23 R1 collocates across corpora
R1
A
C
N
F
surprised
that, by, (to)
if
to, (at, if)
to
surprise (N)
to, (that)
for
to, (that)
to, (and)
surprise (V)
us
me
the
me, him, (her)
admire (V)
the
NN (it)
the
the
anxious (A)
to
NN (to)
to
to
disappointed
by, with, that
with
by
that, (when)
enthusiastic
about
NN (about)
about
about
impressed
by
with
by
by
pleased
to
with
to
to
willing
to
to
to
to
affection
and
NN
for
for
hate (V)
NN (by, the, their, it, to
the, (to)
the, (to, it)
to)
delighted
NN (with)
to
to, (with)
to
frightened
NN (to)
of
of
of
hate (N)
NN
NN
NN
for
As becomes apparent here, three emotion terms are very much LG-stable (collocates shared
by all registers): anxious (to), enthusiastic (about) and willing (to). An additional eight emotion terms are relatively LG-stable (sharing R1 collocates among three registers: either all
written registers or all except academic discourse): surprised, surprise (N), admire (V),
impressed, pleased, hate (V), delighted and frightened. Other than that, we find LG-volatile
terms such as surprise (V) and disappointed. Concerning these, news reportage shares one
collocate with conversation (surprised if), and two with academic discourse (surprise that,
disappointed by) and fiction (affection for, hate the). Conversation (apart from sharing with
news reportage) also shares two collocates with fiction (surprise me, hate it), and one with
academic discourse (disappointed with). Fiction shares with conversation and news reportage
(as noted), and also has one collocate in common with academic discourse (disappointed
that). Summing up all shared collocates, the picture looks as Table A.24 (on the next page)
visualizes. We can see that academic discourse and news reportage are most similar (five
shared collocates). Conversation and news reportage, conversation and fiction, and news
reportage and fiction are also quite similar, sharing three collocates, respectively. This is
perhaps best explicable by the fact that both news reportage and fiction contain dialogue
(quotations by news actors and character dialogue) which mimic conversation, whereas both
22
also involve descriptions of emoters (news actors or characters). Furthermore, the news story
is, after all, also a particular type of story/narrative. Finally, fiction and academic discourse
share two collocates, and conversation and academic discourse share only one,7 being
relatively dissimilar (compare also the findings of the large-scale quantitative analyses in
Chapter 2).8
Table A.24 Shared L1 and R1 collocates across corpora
No of shared L1 and R1 collocates (where variation occurs)
Academic
Conversation
News
aaa(L1) aa(R1)
aaa(L1) aa(R1)
aa (L1) a(R1)
aa(L1) a(R1)
a(L1) aa(R1)
a(L1) aa(R1)
a(L1) a(R1)
a(R1)
a(R1)
Fiction
a(L1) aa(R1)
a(L1) aa(R1)
a(L1) a(R1)
What are the implications of these findings for lexicography? If senses of words differ in
various corpora (Kilgarriff 1997b: 108), and if lexico-grammatical patterns are registersensitive, we could develop dictionaries that reflect this. This could take the form of an
electronic dictionary (with a query system similar to that described by Teubert 2004) that includes five different dictionaries: one ‘general’ dictionary that lists meanings and lexicogrammatical patterns which are common and stable across registers, and four ‘register’
dictionaries (one each for conversation, news reportage, fiction, and academic discourse) that
list meanings and lexico-grammatical patterns that are common in and characteristic for the
respective register. The general dictionary should ideally include frequency bands for each
register (note that the Longman Dictionary of Contemporary English (LDOCE) already makes
a difference between written and spoken language frequency). Importantly, all ‘subdictionaries’ should include detailed information for meanings, patterns and pragmatic functions as well as authentic examples in the given register. Current dictionaries and other learner
sources include a lot of corpus-based information about frequency (Neale 2006: 149-155) and
collocation but this is not always referred to and can then remain relatively worthless to the
user (Sinclair 2004c: 143). And some corpora that are the sources of dictionaries consist
largely of media language such as the Bank of English (Mahlberg 2004: 119), which has some
implications on the resulting description of English.
In other words, what I propose is that the approach that Biber et al (1999) have taken
with respect to grammar can and should be extended to dictionaries, especially in view of the
more or less unlimited space available when they are produced in electronic rather than in
printed form. Thus, when learners read a newspaper or a novel, they can choose the ‘news
23
dictionary’ or the ‘fiction dictionary’; when they want to focus on spoken conversation, they
use the ‘conversation dictionary’; and academic learners will need mostly the ‘academic
dictionary’.9
Other potential applications
Other potential applications of some of the findings in Bednarek (2008) include the development of guidelines or exercises for fiction writing in English, with respect to characterization
and the portrayal of character emotion. It wo uld also be possible to use the affect classification in Chapter 5 for a character analysis à la Toolan (2001). In analogy to Toolan’s proposal
for a character-trait inventory, we could make use of an emotional response inventory. One of
Toolan’s character-trait categories is +/- emotional, and this could be elaborated to incorporate the kind of emotionality that is present or absent (Table A.25 below). In so doing, we can
build up an exact emotional picture of a character, contributing to the analysis of
characterization in works of fiction.
Table A.25
P = affect type present
O = affect type absent
+ = positive affect sub-type present
- = negative affect sub-type present
un/happiness
affection/antipathy
cheer/misery
in/security
quiet/disquiet
trust/distrust
dis/satisfaction
interest/ennui
pleasure/displeasure
dis/inclination
desire
non-desire
surprise
Character 1
P
+ (affection)
- (misery)
O
O
O
P
P
+
P
Character 2
O
O
O
P
O
O
O
O
O
O
-
Character 3
P
- (antipathy)
- (misery)
O
O
O
P
+
P
P
Character 4
O
O
O
P
+
O
O
O
P
Character 5
P
- (antipathy)
+ (cheer)
P
+
O
O
O
P
+
-
Finally, another application outside linguistics concerns the frequency findings of emo tion
terms in Bednarek (2008: Chapter 2). Rather than basing their analyses on elicited or freelisted emotion terms, psychologists and anthropologists could work with those emotion terms
that are most frequently used in conversation.
24
Notes
1
With respect to covert affect note also that not all terms that are derivationally related to
emotion terms should be included as covert affect. With some, the emotional meaning
has been bleached to a large extent; e.g. pity in It is a pity that… should not be coded as
covert affect but rather as judgement even though it shares patterns with covert affect.
Examples of terms that were excluded from the analysis of covert/overt affect are (Table
N.1):
Table N.1: Excluded terms
Excluded
dazzling, nae bother, was a real stunner, jarring, trouble, aspirant
from
members, x is a pain, jollification, buoyant markets, striking,
(covert/overt) strikingly, x was found wanting, pleasant, pleasure, pleasurable,
affect
2
awesome, preoccupation, delightful, their own interests
When analyzing part of speech, a rough distinction was made between the POS categories adjective – adverb – verb – noun – other (relating to idioms). In order to facilitate the
analysis, -ed and -ing verb participles (such as impressed by, admiring, dazzling,
cheered, brightening, much-loved, fed-up with, troubled by, tempted to) were automatically coded as adjectives (compare Bednarek 2008: Chapter 3, but see Osmond 1997:
112).
3
Would you like was analyzed as ‘real’ desire rather than ‘hypothetical’ affection.
4
The terms authorial (1st person) and non-authorial (2nd & 3rd person) affect are suggested
by White (2001a). It becomes clear from White’s discussion that the distinction has to do
with sourcing and the claiming or transferral of responsibility:
•
With authorial affect, ‘the writer is the source of the emotion by which the evaluation
is conveyed and hence takes some responsibility for that evaluation’ (White 2001a: 6);
•
With non-authorial affect, ‘[t]he writer presents herself as merely reporting on the
emotional reactions [of others]’ (White 2001a: 6, emphasis mine); ‘It is, in a sense, an
attributed evaluation, responsibility of which has been transferred to an external
source’ (White 2001a: 6, emphasis mine).
For a discussion that relates non-authorial affect to engagement (attribute: acknowledge)
see Bednarek (2007).
25
5
The approach used in Bednarek (2008: Chapter 3) for the description of affect patterns
was more detailed than local grammar (and less ‘strict’ about the notion of patterns), but
less detailed than FrameNet. It used the local grammar approach to functionally organize
and describe one area of meaning (affect), and the FrameNet approach to include more
detail for the parsing of the affect elements:
Inspired by local grammar
Inspired by FrameNet
description of one area of meaning (a include non-patterns
unified description)
functional orientation
include patterns of items that co-occur with
emotion term
meaning groups
include some collocates that are part of specific (and important) patterns, e.g. expressor
Along the lines of suggestions by Hunston (2003: 16), the functional local grammar approach was used to ‘tidy up’ and ‘organize’ the details emerging from FrameNet and
corpus analyses, and some non-patterns were labelled as local grammar elements (e.g. V
n in n: We admire this characteristic in others, BNC, B19 1079), whereas phrases that
are part of noun phrases/adjective phrases (e.g. Rory hated his mother in a hat, BRC,
G0A 829) were not listed, e.g. as V n in n (though sometimes the difference may be
difficult to capture). The resulting analysis improved the delicacy of the pattern
approach, which ‘given the impressive scale of this work, […] is often rather coarse’
(Stubbs 2001: 460), and was more concise than the FrameNet approach, which can be
overwhelming in its detail.
6
In order to investigate this phenomenon further I analyzed the right-hand noun collocates
for four emotion adjectives in detail: anxious, disappointed, delighted and surprised. Of
these four adjectives, three occur only with nouns that indicate an emoter. This noun is
either a conscious participant (or metonymically an institution, a collective), an expressor, or a linguistic/mental activity:
Disappointed (excluding disappointed expectations)
•
noun = conscious participant (e.g. birdwatcher, competitors, English graduate, ghost,
man, parents, pigeons, NAME, cleric; clubs (metonymic))
•
noun = expressor (e.g. eyes)
•
noun = linguistic/mental phenomenon (e.g. editorial, huffing and puffing, rage)
26
Delighted
•
noun = conscious participant (e.g. grandfather, NAME, boy, fan, parents, Republicans, workers)
•
noun = expressor (e.g. cackle, cascade [of laughter], countenance, face, kisses, laugh,
laughter, squeal, whoop, kisses)
•
noun = linguistic/mental phenomenon (e.g. anticipation, perception, disbelief, outrage, wonder)
Surprised
•
noun = conscious participant (e.g. beast, NAME, servant, young men, bosses, patients, Toronto fan)
•
noun = expressor (e.g. expression, look, fingers, voice)
•
noun = linguistic/mental phenomenon (e.g. approval, reply)
(Surprised also occurs with way modifying an action by an emoter: The surprised way
he had looked at her, BRC, CEC 2373)
The parsing of such adjectives does not entail any major problems: all ADJ n patterns can
be parsed as undirected affect (with some perhaps additionally involving appreciations of
semiotic phenomena).
With anxious, the situation is more complex (even disregarding its use in sexual descriptions: anxious + ‘genital’ as well as one instance of an anxious blue blur in BRC, FRL
1397).
When the right-hand noun collocate points to an emoter, the noun is either a participant,
an expressor or a linguistic/mental phenomenon as with the other three adjectives, or (in
addition) a noun referring to an activity that is clearly related to an actor:
•
noun = conscious participant (e.g. anxious children, clients, individual, patient, people, person, lovers, owners, shop stewards, worker, test takers, wife etc, maybe also
anxious presence)
•
noun = expressor (e.g. expression, eye(s), face(s), features, neck, tone, voice) à
showing the emoter’s anxiety
•
noun = linguistic/mental phenomenon (e.g. questions, reflection, consultations,
discussion, fussing, moan, quaking, account, debate, inquiry, reception, silence,
attention, attachment, spiritual commitment, [mental] state, an anxious shrinking
from everything except duty) à pointing to the emoter’s (sayer’s/senser’s) anxiety
27
•
noun = activity related to an actor (e.g. galvanising the Gnomes into anxious industry, BRC, G1L 79; She put her hands on the bedspread, looked into his eyes, an
anxious searching, BRC, H7F 1228; She took an anxious step towards him, BRC,
BMW 389) à pointing to the emoter’s (actor’s) anxiety
But anxious can also occur with nouns that point to triggers (rather than or in addition to
emoters). One example was already mentioned above:
(F1)
OLDHAM keeper Jon Hallworth is facing an anxious battle to be fit in time for
Saturday’s big Premier League kick-off. (BRC, CH7 869)
In this example, it is presumably the ‘battle to be fit in time’ that causes Jon Hallworth’s
anxiety but the noun also metonymically allows us to infer the emoter (as the one facing
the battle). Anxious frequently occurs with nouns indicating a duration of time, too
(moment(s), days, plane journey, day(s), week(s)). For example:
(F2)
But Roy had thrown away his script, and he spent an anxious plane journey
trying to remember his lines. (BRC, CH1 3108)
(F3)
The 26- year-old midfielder, who played in all three of England’s European
championship games, now faces an anxious wait for Taylor’s verdict. (BRC,
CH7 2804)
(F4)
JONATHAN SPEELMAN gave his supporters an anxious day in the sixth
round of the Pilkington Glass World Chess Championship semi- finals
yesterday. (BRC, A4K 729)
What these examples are saying is that these times (moments, days etc) are spent by
emoters in a state of anxiety because of something that happens during these times. It is
not the days themselves that cause the anxiety, but the happening during these days. For
example, that Roy has thrown away his script causes his anxiety (rather than the plane
journey) or the worry about what Taylor’s verdict might be or Jonathan Speelman’s chess
play in the semi- finals on the day in question. That is, such nouns can neither be parsed
as emoter nor as trigger. Even more complex is an adjective like sad, which can express
both emotion (‘being unhappy’, ‘showing unhappiness of emoters’) with collocating
nouns as emoters or ‘making someone feel unhappy’ with collocating nouns as triggers)
and opinion (‘‘unacceptable, deserving blame’, ‘boring, not fashionable’, ‘in poor condi28
tion’). Collocating nouns cannot easily be used to distinguish these meanings without the
wider context. Thus, a sad man might be unhappy, or ‘boring, not fashionable’ and a sad
consequence can arguably either indicate affect (‘making you unhappy’) or opinion
(‘unacceptable, deserving blame’). The collocates for sad would need to be examined in
detail to find out how accurate they predict the meaning of sad.
7
This points to the fact that conversation is similar in one sense to both news reportage
and fiction (e.g. in containing a certain amount of narrative), while being different in another sense from all written discourse, e.g. in being spoken and interactive (Biber et al
1999: 16). Academic discourse, on the other hand, is sometimes similar to news reportage and fiction (because like them, it is a written register), while also differing from
conversation, news reportage and fiction in that it presumably does not contain as much
narrative and hardly any dialogue.
8
These findings might change if the analysis of patterns is broadened to positions other
than L1 and R1 and to patterns that are less frequent in each register (compare Bednarek
(2008: Chapter 4) for the methodology of the analysis of lexico- grammatical patterning).
9
More detail would also be helpful where emotion adverbs are concerned, which are often
only run-on entries in dictionaries on account of their infrequency, though they have a
variety of different meanings (see Appendix 2 for some of the meanings of desperately,
happily, cheerfully).
29
