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Sociologique
Bulletin de Méthodologie
http://bms.sagepub.com/content/70/1/5The online version of this article can be found at:
DOI: 10.1177/075910630107000103
2001 70: 5Bulletin de Méthodologie Sociologique
Roel PoppingModal Auxiliaries in Text Analysis
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5
MODAL AUXILIARIES IN TEXT ANALYSIS*
by
Roel Popping(Department of Sociology, Groningen University,
NL 9712 TG 15; [email protected])
Résumé. Les auxiliaires modaux dans l’analyse textuelle. Les verbes auxiliaires modaux tels
que devoir, vouloir... fournissent des informations concernant les intentions des sujets sémantiquesdans les morceaux de phrase où ils apparaissent. Par exemple, en déclarant qu’une personne doit
agir d’une certaine manière, on montre la différence entre l’action et la possibilité que la personne
agisse autrement. Cette information peut être utilisée dans la recherche. L’analyse textuellesémantique permet le codage des auxiliaires modaux. Cet article examine comment faire pour
garder en présence les auxiliaires modaux lors une analyse textuelle par réseaux. Ce type d’analysetextuelle nous permet de traiter des argumentations assez complexes, mais le résultat montre quele chercheur ne doit pas utiliser l’analyse par réseaux dans ces cas et plutôt employer l’analysesémantique. Analyse textuelle sémantique, Analyse textuelle par réseaux, Verbes auxiliaires
modaux.
Abstract. Modal auxiliary verbs (e.g., ought, want, etc.) convey information about the intentions
of the semantic subjects within the clauses in the text in which they appear. For example, in
asserting that a person ought to act in a certain way, one contrasts the action with the person’s
potential intentionto act
otherwise.This
qualitycan be used
in research. Semantictext
analysisallows coding modal auxiliaries. This paper investigates how to make modal auxiliaries remain
visible when network text analysis is used. This type of text analysis allows one to deal with rather
complex argumentation. The answer shows that in this situation network text analysis should not
be used, the investigator should stay with semantic text analysis. Semantic Text Analysis,Network Text Analysis, Modal Auxiliary Verbs.
INTRODUCTION,
~
- &dquo;
Classical (or thematic) text analysis only counts the occurrence or
co-occurrence of words, phrases or themes as they are found in
blocks of text. More recent approaches like semantic and networktext analysis concentrate on the relation between themes. This givesadditional information. First of all, it is certain that a relation exists
between the themes. In the thematic approach, this was onlyassumed, since themes occurred in the same block of text. Secondly,
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the relations are named. The structure of such a relation is
Subject(Verb(Object (S(V(O).Often the relation is also
weightedor
valenced (&dquo;He loves her&dquo; is stronger than &dquo;He likes her&dquo;, and &dquo;Hehates her&dquo; is the opposite). Now the relation becomes
Subject(Valence(Verb(Object (S(V(V(O).
Modal auxiliaries express a specific kind of relation. They denotesome intentionality. They may be part of some political or ideologicaldiscourse. Modal auxiliary verbs cannot be simply valenced, one can
not say in advance that &dquo;must&dquo; is always stronger than &dquo;should&dquo;.This valencing and the use of certain modal auxiliaries depend on
the context in which they are used; for example, in a culture or a
political system. Modal auxiliaries can be recognised when semantictext analysis is performed. The question is whether they can also be
captured in network text analysis. This type of analysis is attractive
as it concentrates on complete argumentation instead of individualsentences or clauses.
As we are investigating popular developments in the transition fromformer Communist-lead Central-Eastern European countries to
more democratic states (Popping and Roberts, 1998), we will
concentrate on the political system. In this context, not all modalauxiliaries are relevant.
REPRESENTATION OF TEXTS
’
.,
~..
In text analysis today, three methods are used to represent the texts
that are analysed. For a long time, researchers have been consistent
intheir
portrayalof
text analysisas
involving the quantification ofqualitative data (e.g., words, gestures, art forms, etc.) for the
purpose of permitting statistical inference (Krippendorff, 1980;Popping, 2000). There are only a few differences between theseresearchers with respect to the (especially manifest versus latent)nature of their subject matter. Computer programs following the
approach of these researchers provide users with &dquo;counts&dquo; of wordsor phrases within blocks of text (e.g., newspaper articles,transcribed speeches, etc.). Such word counts are then aggregatedaccording to a dictionary of theoretically expedient meaning
categories (or themes), theme occurrences can be used to makestrong inferences regarding thematic differences across varioussocial and temporal contexts (e.g., Weber, 1990; Popping, 2000).Hereafter, I will use the term &dquo;concept&dquo; to denote a single idea
represented by a single word or a phrase. It is the basic unit for the
meaning content of a piece of text. The term &dquo;theme&dquo; is usually usedfor broader classes ofconcepts.
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Since the late 1980s, a number of social scientists developed their
own methods not only for identifying concepts, but also for encodingthe relations among concepts in texts that thematic text analysisignores (Carley, 1986 & 1988; Van Cuilenburg, Kleinnijenhuis and
De Ridder, 1986 & 1988; Franzosi, 1989; Roberts, 1989). Theseresearchers’ methods for encoding texts are strikingly similar. In
each case, S(V(V(O meaning relations among words are encoded as
they appear in clauses of the texts under analysis. A clause is a partof a sentence with its own inflected verb and associated subject and
object. A sentence might consist of several clauses. Instead of
sentence, the more general term &dquo;discourse&dquo; is also used. A
discourse isa collection of
statementsin a text in
whichrelations
are made between subject and object.
Inflected verbs can be recognised as all words that change form
when the person and/or tense of the clause are changed. Anexample: The word &dquo;go&dquo; in &dquo;I go&dquo;, and the word &dquo;goes&dquo; in &dquo;He goes&dquo;are inflected verbs because they change form when the subjectchanges from first to third person.
Unlike thematic text analysis, such &dquo;clause-based text analysis&dquo;affords inference about how texts’ sources use words in their speechor writing. Where the social scientists’ methods differ is in the
research purposes to which the relationally encoded texts are
applied. &dquo;-..’ ; : - ..’~ ~ ... , ... -... , . _ . - ,..
’’ &dquo;
.~...:&dquo;~ &dquo;
..... , -...
SEMANTIC APPROACH .. , , I - &dquo;-
The first of the newer
approachesis semantic text
analysis.This
method yields data and inference about the intended (as opposed to
the manifest but more superficial grammatical) relations among
words in various socio-temporal contexts (Roberts, 1997a & 1997b).Whereas thematic text analysis concentrates on word or phrasecounts, this type of analysis looks at theme relations. Each clause is
coded according to the meaning that it was intended to convey;
namely, as a description or as a judgement of a process or of a state-
of-affairs. Each of the resultant types of intention (description of
process, description of state-of-affairs, judgement of process,
judgement of state-of-affairs) has associated with it a distinct, butunambiguous semantic grammar. Once a clause is encoded
according to one of these four semantic grammars, the available
computer program reconstructs that clause by &dquo;translating&dquo; it
according both to the meaning categories into which its words fall,and its words’ intended interrelations. For example,
..
__, ... , ._ ,. , .,...,.
( I’ ’ ’ ’,
I’
°. J,°_ , ’
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SOMEBODY is obligated to SUMMARIZE a badness/harm (that THEPAST_GOVERNMENT FACILITATED) and SOMEBODY is obligated to
SUMMARIZE a LESSON about a badness/harm.
is a translation of the following sentence, also to be used later in
this text, from an article in the newspaper, N6pszabadsag, of April14, 1992:
Somebody should summarise for each new government the mistakes
made by the previous government, and the lessons of these
mistakes.
Such translations allow the coder to evaluate the face validity of the
encoding. Semantic text analysis yields inferences abouttoccurrences of specific statements among socio-temporal contexts of
theoretical interest (e.g., democratic imagery in Eastern Europeaneditorials prior vs. following the recent collapse of their Communist
governments). The objective is not to capture logical arguments or
cognitive maps, but to estimate the probability that specific classes
of statements occur. The technique is useful for comparingstrategies of communication in different socio-historical settings,and for measuring shifts in public opinion (or public perceptions)when texts have grass roots sources.
The above illustration shows that modal auxiliaries are captured inthe coding process. The computer program, PLCA, codes them as
separate variables (Roberts, 1989). The clause is the record unit.
This example contains two of such units. The first one contains themodal auxiliary verb &dquo;ought&dquo; (or to be obligated to). This computerprogram also allows the user to encode, among other things:
the syntactic form of the clause,.
the semantic subject of the verb,the modifier of the subject,the verb,
-
’
.
the tense, .
’
the semantic object of the verb,. &dquo;..
the modifier of the object, and
the valence.’
’
’
Each clause’s syntactic form reflects the author’s intended meaningas a description or an evaluation in reference to a process or a state
of affairs. The texts to be analysed are obtained by applying a
sampling procedure.~ ~
~_~ ~
~ ~
When a clause is used, its sender and intended audience are often
known. In written editorials, the journalist is usually the sender,and the people reading the newspaper or listening to the radioconstitute the audience. This sender-audience pair remains
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constant within any particular editorial. In situations where, for
example,the
primeminister of one
countrytells the
presidentof
another country what to do, it is important to know both sender and
audience. In computer programs for semantic and network text
analysis, the sender and audience can be coded.
NETWORK APPROACH ..- .
The Network analysis of Evaluative Texts (NET) approach provides
data on latent propositions that can be logically derived from texts’manifest content (Van Cuilenburg, Kleinnijenhuis and De Ridder,1986 & 1988). The approach permits inference about how such tacit
propositions are related to the social contexts within which texts are
authored. Map Extraction, Comparison, and Analysis (MECA)provide data and inference regarding similarities and differences in
the ways that groups of individuals relate (i.e., cognitively maps) to
various aspects of their worlds (Carley, 1986 & 1988). In these
analyses, the network positions are relevant. The NET approachlends itself more readily than MECA to analysing the modal
auxiliaries within texts.
The network evaluation approach has its roots in the work on
evaluative assertion analysis by Osgood et al. (1956). The
fundamental premise of this methodology is that every language hasthree kinds of words:- &dquo;Common meaning terms&dquo; These are words that have a
common evaluative meaning among &dquo;reasonable-sophisticated users
of the language&dquo;. For example, the common meaning of words such
as &dquo;peace&dquo; are always positive; whereas that of words like &dquo;enemy&dquo;are always negative in connotation.- &dquo;Attitude objects&dquo; These have no fixed evaluative meaning. For
example, a word like &dquo;car&dquo; is likely to be evaluated differently bydifferent people.- &dquo;Verbal connectors&dquo; These are words that indicate the
association (&dquo;it is...&dquo;) or dissociation (&dquo;it is not...&dquo;) of attitude objectswith common meaning terms or with other attitude objects.
By investigating how attitude objects are associated or dissociated,one can investigate how these attitude objects are valued in a text.
In brief, the method requires that texts be parsed into clauses, that
instances of the three above-mentioned word-types be located withinthese clauses, and that the clauses be recombined in a way that
reveals structure in the text. The NET text analysis method goesbeyond this approach to reveal the logically implied structure of
texts as well.
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Two important steps are required when one encodes networks from
texts. First,one
must specify the concepts whichare to
be relatedor
linked within networks. These concepts may originate in one’s
theory or in the texts themselves (i.e., with concepts that are
empirically recurrent). Encoding then involves the classification oftexts’ words/phrases as occurrences of these concepts. Yet this
encoding process is likely to generate invalid data if contextual
information is not taken into consideration. Thus, it is crucial that
native speakers determine when specific words/phrases are
instances of one concept, and, given their idiomatic usage, when
they are instances of another.
The second step in network encoding involves the assignment of
links between pairs of concepts. After removing idiomatic ambiguityduring the classification of concept occurrences, most interrelations
among words/phrases can be unambiguously identified according to
the grammar of the language in which the texts were written. Yet
even identical non-idiomatic, but grammatically-correct statements
can have different meanings, depending upon the meaning intended
by the statement’s source. For example, the statement &dquo;Joe was
abandoned&dquo; could mean either that Joe was
aloneor that others
departed. All relational text analysis methods must ensure that such
illocutionary ambiguity is systematically disambiguated (Roberts &
Popping, 1996).
Within any single network, no concept appears more than once. As a
direct consequence, graphical displays of networks become complexwhen specific pairs of concepts are allowed multiple types of links.
For this reason, network analysis software developers have tendedto gravitate toward six general types of links (Popping & Roberts,
1997):*
Similarity - records that one concept is identical with another. Therelation is symmetric (abbreviations: ALIke, SIMilarity, EQUal,EQuiValent);*
Causality - denotes a cause-effect relation. The relation is
asymmetric and transitive. In all methods using networks based on
text, the causal relation is read as &dquo;might&dquo; cause (CAUses, is Caused
BY);* Relation - indicates an ASSociation, an ORDering, an EVAluation,or a REAlization. In the first case the relation is
symmetric,in the
other three it is asymmetric;* Classification - indicates transitive (is A Kind Of, Has As Kind),asymmetric (Is Property Oi), or symmetric (INConsistent with or
contradicts, DIStinct) classification;* Structure - indicates a structuring. The relation is transitive (isPARt of, Has As Part);* Affective - establishes a judgement of the subject about the object(AFFective, WILL). ,
,
.
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Several software
applicationsalso allow
recordingthe
degreeof
similarity, causal determination, etc. of links, usually on a ( to (11scale. For example, &dquo;He hates her&dquo; would be valenced with (1, the
&dquo;He loves her&dquo; with + 1. The &dquo;He likes her&dquo; is more than a neutral
statement, so it might be valenced with +0.5. In the positivedirection, there are usually only two valences: +0.5 and + 1. All three
sentences are of the affective type. Evaluative links (&dquo;this is good&dquo;)can often be encoded on a minus-neutral-positive scale. Moreover,statements may themselves be conditionally encoded. For example,they may be encoded as &dquo;if statement 1 then statement 2&dquo; or as
&dquo;statement 1 or statement 2 is true&dquo;. The sentence: &dquo;If theCommunists do not win the elections, democracy has a chance&dquo; is
encoded as:
Clause: democracy / [condition] has (+) / chance ....
Condition: Communists / do not win (+) / elections.
The NET approach also uses two special-purpose concepts. These
concepts are related to the distinction between &dquo;attitude objects&dquo;(words with no fixed evaluative meaning) and &dquo;common meaningterms&dquo; (words that have a common evaluative meaning among
reasonably-sophisticated users of the language). Usually, a
statement can also be encoded as a positive (is good) or negative (isbad) evaluation of a concept by relating it to the abstract concept,&dquo;Ideal&dquo;. For example, the statement &dquo;the man is friendly&dquo; is
reformulated into &dquo;the man has a good relationship with the Ideal (ofthe statement’s source)&dquo;. By connecting a concept to the concept&dquo;Real&dquo;, a researcher can encode a statement as an affirmation that a
concept’s referent exists (is) or does not exist (is not). The statement
&dquo;unrest is rampant&dquo; is abbreviated as &dquo;Reality shows a high level ofunrest&dquo;. This implies that a concept can also be encoded in an
abstract manner.
A network represents an argumentation presented, for example, in
an editorial in a newspaper. Such a network can be reduced to a
simpler one, as is shown in Figure 1. Say the network contains three
concepts, namely A, B, and C. A positive valued relation exists from A to C, a negative valued relation from A to B, and two negativevalued relations from B to C. The four arrows are shown in the left-
most arrow network. In the link network, the two arrows from B to C
are reduced to one. In the chain network, chain A-B-C is reduced to
A-C. Finally, the two relations between A and C are combined in thebundle network. For details, the reader is referred to De Ridder
(1994) or Popping (2000). Carr6 (1979) provides the mathematics on
which these aggregations are based. In network text analysis, a
complete reasoning (for example, as presented in a paragraph in an
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editorial) might be investigated. This is different from semantic text
analysiswhere
onlytendencies are
investigated.
Figure 1 - Network aggregations . ,.
MODAL AUXILIARY VERBS &dquo; .
Generally speaking, modal auxiliary verbs are verbs that are usedwith (usually, the infinitive form of) another verb to express
possibility, necessity, probability, certainty, etc. They are not used to
talk about things that definitely exist or events that definitelyhappened. The meanings of the verbs are grouped together in
several ways. In a sense, two main groups are distinguished:degrees of certainty (certainty, probability, possibility, choice) and
obligation / freedom to act (ability, necessity, permission, politerequest). This list is not exhaustive.
In text analysis focusing on democratic imagery, a firm restriction is
posed on these auxiliaries. First, they have to express intentionality
(e.g., they may describe how the future society should look), and notactuality (as in &dquo;Tomorrow the parliament will vote for...&dquo;). This
intentionality is confined by ego’s needs or alter’s morality, or
intentionality enabled by ego’s potentiality or alter’s obligations. An
example is found in the statement: &dquo;We must get more freedom to
express our opinions&dquo;. Second, the auxiliaries are used to express
implausibility or plausibility that ego’s intentions will be realised. An
example of plausibility is: &dquo;I can do it&dquo;. Therefore, modal auxiliaryverbs must indicate both (im-) plausibility and intentionality. For
example, the sentence &dquo;The washing machine can clean clothes&dquo;
indicates plausibility but not intentionality, whereas &dquo;He can cleanclothes&dquo; indicates both (as long as the sentence is not intended to
deanthropomorphise &dquo;he&dquo; as machine-like). Although, from a
grammatical standpoint, the verb &dquo;to be able&dquo; is a modal auxiliaryverb, for the purposes of encoding texts, verbs that merely indicate
degrees of certainty, probability, possibility, impossibility, etc., are
not the type of modal-intentionality indicators that are sought here.
Illustrations of sought-after modals are listed below:
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-
Ego’sneeds Must (not in case of non-persons; e.g., &dquo;leaves must
fall&dquo;), refuses, would (not in case of mere possible futurity), etc. &dquo;The
politician would lose the vote if she failed to lobby for her case.&dquo;
- Alter’s morality May (in the sense of &dquo;permission&dquo; not
&dquo;possibility&dquo;), attempts, might (not in case of mere possible futurity),etc. &dquo;They might change the plan.&dquo;
- Ego’s potentiality Can (not in case of non-persons; e.g., washingmachines), wants, could (not in case of mere possible futurity), etc.
&dquo;I can do it.&dquo;
- Alter’s obligation Ought, hopes, should (not in case of mere
possible futurity), etc. &dquo;Next year I expect that the administration
should reduce taxes.&dquo;
Such modal auxiliary verbs can only be coded validly when the
context in which they are used is taken into account. This impliesthat the re p resentational view of coding is to be followed. Here,coding is performed by a human coder who codes a message from
the perspective of its sender. This is distinct from instrumental
coding, where &dquo;automatic&dquo; coding is performed from the perspectiveof the investigator (Shapiro, 1997). Another characteristic of the
representational way of coding is that it can overcome problems due
to ambiguity in language.
Many sentences or phrases do not explicitly contain a modal
auxiliary verb, but implicitly they do. In such cases, a specifictransformation to a modal form must be applied. Some examples
follow:
X &dquo;only makes sense if’ Y. --> IfY, then X ought to happen.X &dquo;is afraid that&dquo; Y. --> X does not want Y.
’What&dquo; X &dquo;needs is&dquo; Y. --> X ought to have Y.
X’s &dquo;plans to do&dquo; Y &dquo;are unrealistic&dquo;. --> X cannot do Y.
In the coding process, such transformations are made almost
automatically, otherwise the coding itself almost becomes
impossible. The hypothesis is that the type of modal auxiliary verb
used by a country’s citizens is related to the country’s type of
political system. In particular, modal auxiliary use differs amongauthoritarian, capitalistic, or social democratic states. This
hypothesis will be examined elsewhere.’
. &dquo;
’
,
,,,.
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MODAL AUXILIARY VERBS IN NETWORKS
As indicated before, modal auxiliary verbs must indicate both (im-)plausibility and intentionality. This implies that, in terms of theNET approach, they refer to some ideal situation. In the network,this becomes visible in the relation between a specific concept and
the concept, &dquo;Ideal&dquo;. In case a sender or a receiver is known,additional information is added. In this case, one knows that these
specific parties share a specific Ideal. The valence will usually be
extreme, either -1 or + 1. For example: &dquo;The leader of the party stated
thatwe
should get more freedom&dquo; might be interpreted as &dquo;...weought to get more freedom&dquo; or as &dquo;...we shall likely get more freedomin the future&dquo;. Only the first interpretation involves the use of a
modal because it indicates both plausibility and intentionality. With
respect to coding for a network analysis, the sentence is read as a
universal statement spoken by the leader of the party: &dquo;Freedom is
an ideal.&dquo; As an S-V-V-O statement, it would be represented as
follows:
(Source) leader party: &dquo;(Subject) freedom / (verb) is (relation: EVA +1)/ (object) an ideal.&dquo;
Most sentences do not contain such universal statements. For
example, &dquo;The administration should reduce taxes&dquo; is rendered as
&dquo;The administration, if the administration reduces taxes, is actingwell&dquo; or as the following S-V-V-O statement:
&dquo;Administration / if (administration / reduces / taxes) (ACT -1) /Ideal.&dquo;
&dquo;
Note that the relation between administration and taxes isconsidered as a negative one. Say the chance that the condition willbe fulfilled is estimated as 40%; this is finally coded in CETA as: ,
&dquo;Administration (0.4) / (EVA + 1 ) / Ideal.&dquo; /.
In general, the modal auxiliary verbs are found in the following typesof links: similarity, cause, and association. But with the project on
imagined democracy, restrictions are imposed. These require that
similarity and cause be not used. The modal auxiliary verbs, as
described, are found in statements in which one of the concepts isthe Ideal. The type of relation here is an evaluation, which isconsidered as a special kind of association. This relation might bevalenced either positive or negative. In the case where the sentenceis read as &dquo;The administration reduces taxes&dquo;, different results willbe found:
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&dquo;Administration / reduces (ACT -1 ) / Taxes.&dquo; .:
Here, the condition is lost completely. Therefore, this alternative is
not allowed. In network analysis not based on the NET approach,sentences are read as in this example.
Links ’
’
― ’- ~’ &dquo; ;&dquo;.i . . ~_: ~ ’ . ’ > .. &dquo;
- ~ ~ ...
..:.> > ...... , ... : :.’: ., ... ,.. :
When aggregation starts, one of the relation types involved is the
evaluation. In the aggregation process, the evaluation is consideredas an association. In case the relations are linked, the resulting typeis the type of the other relation (i.e., association and other typealways results in other type) (De Ridder, 1994: 98; Popping, 2000:
112). The following example is a paragraph from an editorial
entitled, &dquo;Brand-Old Lessons&dquo;, in the Hungarian newspaper,Nepszabadsag, of April 14, 1992:
&dquo;Somebody should summarise for each new government the
mistakes madeby
theprevious government,
and the lessons of these
mistakes; during a change of political systems [tr.: somebody should
summarise] the previous system’s offences in style or manner (and I
will not even mention crimes), including the one-sided personnelpolicies when the previous system was started, the foolish priority of
politics, and the negative consequences of this [tr.: politics-dominated society] that lasted for long years, decades.&dquo;
&dquo;
The relation:&dquo;
.
,. .~. :.. ’.~) _ :~~:~::j
&dquo;somebody / if (....) (ACT -1) / Ideal&dquo;-
’ &dquo; ’ =’-,&dquo; .’ .’.&dquo;&dquo;;
is found at least three times. When these relations are linked,nothing changes. The linked relation is:
&dquo;somebody (0.4) / ASS -1 / Ideal.&dquo;, - ;.’ ,’,’ &dquo;..
&dquo;
~ - -~ - &dquo;’
assuming a chance of 40% that these things will be summarised. In
linking, EVA + EVA, or more general ASS + ASS, is used whichresults in ASS. Note that here
only specificforms of association
relations have been joined so other types are in fact excluded. If
sentences are also coded that do not contain modal auxiliary verbsas treated here, it is not excluded that other types of relations will befound between &dquo;somebody&dquo; and &dquo;Ideal&dquo;. In the aggregated network, it
is now possible that the final relation between the two concepts is of
another type than association. However, no concrete examples were
found in the sampled editorials.
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Linking, and also chaining and bundling, has far more
consequences.It influences the valence. It is also useful to
saysomething about the quality of the resulting relations. For this typeof information, the reader is referred to De Ridder (1994) and
Popping (2000). The investigator has to decide here how modal
auxiliary verbs related to the original relations will be aggregated.This is possible if the number of modal verbs is large. Otherwise,information about the modal verb itself will usually be lost. CETA
provides no convention for deciding what the result of &dquo;could&dquo; plus&dquo;should&dquo; will be.
Chains
~~ ~
..
With regard to chains, the resulting type is always an association
(i.e., association with other type results in association) (De Ridder,1994: 97; Popping, 2000: 112). Above, we had a statement in which
freedom is evaluated as an Ideal. The &dquo;saying what you want to say&dquo;might be coded as a part of freedom. Now we have two statements:
freedom / EVA + 1 / Idealsay what you want / PAR + 1 / freedom.
The PAR (is part of) relation is a special case of the similarityrelation. The resulting chain is:
.
say what you want / ASS + 1 / Ideal.
This comes from PAR x EVA, or SIM x ASS, which results in ASS.
The concept under investigation, however, is nearly always found inthe subject part of the statement. The object part contains the Ideal,just as in the situation of linking. In the editorials I investigated, thelatter situation always occurred. The fact remains that the objecttype is the Ideal. Based on the same argumentation as before, themodal auxiliary verbs need to be counted, based on the originalnetworks.
A problem that is relevant with regard to chaining is that of
transitivity. The reasoning that is followed should be correct. De
Ridder (1994: 64) presents the following example:
Boredom leads to vandalism.~ .. , .
.
Vandalism is punishable.
Multiplying the sentences would yield: &dquo;Boredom is punishable.&dquo; Theissue is not whether boredom is punishable, but whether the
negative common meaning &dquo;punishable&dquo; is transmitted by an
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argument to &dquo;boredom&dquo;. I will not go further into this discussion
here.
Bundles .
Bundles result from linking and chaining. Due to chaining, the final
relations are in general of type association. In the bundles, the final
object type is again the Ideal. The modal auxiliary verbs occur in
combination with this type. It was stressed previously that the
counting of the modal auxiliary verbs should be based on theoriginal networks. These verbs are usually related to the object,Ideal. Therefore, it is possible to relate the counts to this concept.
The main question was whether network text analysis might be
preferred rather than semantic text analysis in the analysis of
democratic imagery. In semantic analysis, one codes the semantic
subject and object of the verb, plus the verb and its modal auxiliary.In network analysis, the occurrence of the various modal auxiliaryverbs is related to a concept. The (aggregated) network contains
other information than the clause as coded in semantic analysis.Modal auxiliary verbs are more explicitly depicted in the coded
clauses than in the coded networks. Therefore, when the modal
auxiliary is a dependent variable, it seems that the data should be
collected by applying semantic text analysis and not by applyingnetwork text analysis.
EXAMPLE .
,!
The above paragraph from the editorial, &dquo;Brand-Old Lessons&dquo;, in
Népszabadsag, of April 14, 1992, which is a very complex one, is
analysed by applying network text analysis (CETA) and semantic
text analysis (PLCA). The differences in results are discussed below.
We start with the network analysis by coding as follows: &dquo;Somebodyshould summarise for each new government the mistakes made bythe previous government, and the lessons of these mistakes.&dquo;
Somebody / (EVA; + 1.0 ~) / Ideal’
’
Somebody / (EVA; + 1.0 -) / Ideal .
Reality (= Previous_Government) / made (REA -1.0) /Mistakes
...,;..
Note the sentence consists of two parts, one about the mistakes and
one about the lessons. The relations as presented here only hold in
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case the condition is fulfilled. Once this means that the mistakes are
summarised, andonce that the
lessonsare
summarised. Conditionsin CETA are indicated by the tilde (-).
&dquo;During a change of political systems somebody should summarise
the previous system’s offences in style or manner (and I will not even
mention crimes), including the one-sided personnel policies whenthe previous system was started, the foolish priority of politics, and
the negative consequences of this politics dominated society that
lasted for long years, decades.&dquo;
All non-essential prepositional and other phrases are dropped.These include, &dquo;During a change of political systems&dquo;, &dquo;in style or
manner&dquo;, and &dquo;for long years, decades&dquo;. The investigator also has to
locate idiomatic phrases and render them according to their
intended meaning. For example, &dquo;and I will not even mention crimes&dquo;is not literally what it means. It probably is an ironic statement thatmeans precisely its opposite; namely, &dquo;I am mentioning crimes.&dquo; The
sentence, &dquo;consequences of this politics dominated society that
lasted&dquo;, is not clear. It could mean &dquo;lasting consequences of prioritiesfor a
politics-dominated-society&dquo;, &dquo;consequencesof
priorities fora
lasting politics-dominated-society&dquo;, or &dquo;consequences of this politicsdominated a lasting society&dquo;. For the coding process, this will beread as &dquo;consequences of these priorities that lasted&dquo;. This leaves us
with the following coding:
Somebody / (EVA; + 1.0 -)/ Ideal
Somebody / (EVA; + 1.0 -)/ Ideal,
Somebody / (EVA; + 1.0 ~) / IdealSomebody / (EVA; + 1.0 -)/ Ideal ..
Reality (= I) / mention (REA + 1.0) / Crimes
This sentence is coded as consisting of five parts, four of which are
conditional. The conditions refer to the system offences, the
personnel policy, the priority of politics, and the negativeconsequences.
In total, there are seven relations now. Assuming each time a
conditional chance of 1, the condition will take place, the number of
conditional relations is reduced to one afterlinking.
This linked
relation is of the type ASS (association). From these results, we
know that six modal auxiliaries are used. We do not know whetherthese auxiliaries are of the type we are looking for, and we do not
know which subject and object are involved. If the modal auxiliarieswere not taken into account, one would find six relations where
somebody is the subject and mistakes, lessons, offences, policies,priority and consequences are the objects. All these relations are of
type ASS and are valenced -1.
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Analysing the text by using semantic text analysis, here PLCA,results in the
following codingfor the first sentence:
SOMEBODY is obligated to SUMMARIZE a badness/harm (.50) (thatTHE PAST_GOVERNMENT FACILITATED (1.00)) and SOMEBODY is
obligated to SUMMARIZE a LESSON about a badness/harm (.50).
This closely follows the original sentence’s syntax. The sentence has
two inflected verbs, namely &dquo;should&dquo; and &dquo;(implicitly: were) made&dquo;. If
we eliminate the prepositional phrase &dquo;for each new government&dquo;,this leaves: Somebody should summarise the mistakes (clause
relative to mistakes: that the previous government made) and(implicitly: somebody should summarise) the lessons of these
mistakes. As the parts &dquo;somebody should summarise&dquo; are mentioned
twice, they are weighted 0.50, which is mentioned between brackets.
When dealing with the network approach, some steps in the codingprocess were already mentioned. Here, the following is added. In the
semantic analysis, the first step is to identify each of the text’s
inflected verbs. In the second sentence, these are &dquo;should&dquo;, &dquo;will&dquo;,
&dquo;was&dquo;, and &dquo;lasted&dquo;.In
fact,there is
anotherinflected verb that is not
explicitly stated. The conjunctive clause &dquo;when the previous systemwas started&dquo; is not semantically linked to &dquo;including the one-sided
personnel policies&dquo;. In other words, the time when &dquo;the offences
included policies&dquo; was not at the time that the past system started.
A more explicit statement would be something like &dquo;including the
one-sided personnel policies (that occurred) when the previoussystem was started.&dquo; The final step is an attempt to preserve the
relations between the clauses as much as possible. The second
sentence becomes after coding:__
.. _ ,’., ,.__ ,,
SOMEBODY is obligated to SUMMARIZE THE
PAST_GOVERNMENT’s OFFENCE (.33) (that is a bad
PERSONNEL_POLICY (that OCCURRED (EXCLUDE) when [there wasTHE PAST_GOVERNMENT’s BEGINNING (.33)1)) and there is THE
CRIMES (.33).SOMEBODY is obligated to SUMMARIZE THE
PAST GOVERNMENT’s OFFENCE (.33) (that is a bad
POLITICS_AS_PRIORITY (.33)) and there is THE CRIMES (.33).
SOMEBODY is obligated to SUMMARIZE THEPAST_GOVERNMENT’s OFFENCE (.33) (that is a CONSEQUENCEfor THE DOMINATED_SOCIETY (1.00) (that was a DURATION (1.00)))and there is THE CRIMES (0.33).
The sentence is coded as three sentences, each part weighted 0.33,but, in the analysis, the sentence is still treated as one sentence. In
total, one clause is excluded from analyses. Now 15 clauses are left,but, when weights are taken into account, this total is 9 clauses. In
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this latter situation, we find two clauses having depth zero; i.e.,there are two main clauses. Both main clauses contain a modal
auxiliary verb, &dquo;ought&dquo;. In the first of these main clauses, the subject&dquo;somebody&dquo; is linked to the object &dquo;bad&dquo;; in the second main clause,the same subject is found, but this time linked to the object, &dquo;the
offence&dquo;. These relations allow us to examine which combinations of
subjects and objects are found in combination with a specific modal
auxiliary. It is up to the investigator to determine whether or not a
modal auxiliary belongs to one of the classes mentioned earlier;these are ego’s needs, alter’s morality, ego’s potentiality, and alter’s
obligation.
The network text analysis does not determine these relations. Here,no weighting is allowed, only one type of relation results
(evaluation), and the number of clauses is different. It might be that,in the network analysis, the first evaluative statement should not
have been added. This is a decision to be made by the investigator. It
has no consequences for the final conclusion. In case modal
auxiliaries are to be investigated, the investigator should use
semantic text analysis.
CONCLUSIONS
We investigated whether network text analysis should be used to in
the situation where modal auxiliary verbs contain a lot of relevant
information with regard to the topic under study. It turns out that it
is hard to capture the meaning of modal auxiliary verbs using theCETA approach to network text analysis. Therefore, the other type of
text
analysis,the
semantic one,is to
be usedin
this type ofinvestigation.
The problem of selecting sentences containing the desired modal
auxiliary verbs incorporated in the sample has not been treated.
This selection process must be agreed upon. Starting with network
analysis, the investigator might use (random or stratified) sampleparagraphs and code all sentences (to catch the argumentation), or
only code the sentences that contain the desired verbs. In case
semantic text analysis is used, it seems sufficient to code the
sentences containing the desired verbs. What is left is that all thishas to be tested in empirical research.
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NOTE &dquo; ~ ― ’. ’-’....
* An earlier version of this text was presented at the Fifth
International Conference on Logic and Methodology, Cologne, 3-6
October 2000.
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