Compositional approach applied to analysis of attitudes
expressed through written language
Alena Neviarouskaya
University of Tokyo

Helmut Prendinger
National Institute of Informatics, TokyoObjectives

Mitsuru Ishizuka
University of TokyoApproacho classification of sentences according to the fine-grained

We developed @AM (ATtitude Analysis Model) system,
which automatically recognizes affect, judgment, andappreciation in text.

o analysis of the strength of the attitude
o determination of the level of confidence, with which theThe algorithm for attitude classification is designed based on
the compositionality principle and a novel approach relying on
the rules elaborated for semantically distinct verb classes.

We set three-fold focus in our research:

attitude labels

attitude is expressed.

Architecture and Principles of @AM (ATtitude Analysis Model)text@AM manager
Sentence
SplitterSymbolic Cue
Processing

Analysis of
Syntactic
Structure andFunctional
Dependencies

Parser Output
Processing

Word Level
Analysis

WordNetAnalysisy ofConcept
Domains

Lexicon for
Attitude Analysis

Clause Splitter
Formation
Builder
Representation
off ClauseCl
Dependencies

PhraseLevelAnalysis

Clause andSentence
LevelAnalysis

text withattitudeannotations

Rules based oncompositionality principle andsemantically distinct verb classes

Fine-grained attitude labels:
affectjudgment
appreciation‘anger’ ‘disgust’ ‘fear’ ‘guilt’ ‘interest’ ‘joy’ ‘sadness’ ‘shame’ ‘surprise’ ‘POS jud’ ‘NEG jud’ ‘POS app’ ‘NEG app’ ‘neutral’We built the lexicon for attitude analysis that includes:(1) affect-conveying symbolic cues (emoticons and abbreviations)(2) attitude-conveying
attitude conveying terms:
POS
adjective
adjective
adverbnounverb

Wordhonorableunfriendly
gleefully
abnormality
desireConsideration of the Sementics of Verbs

Category: intensity
POS jud: 0.3
NEG aff (sadness): 0.5; NEG jud: 0.5; NEG app: 0.5
POS aff (joy): 0.9
NEG app: 0.25
POS aff (interest): 1.0; POS aff (joy): 0.5(3) modifiers:• adverbs of degree (‘significantly’, ‘slightly’) and adverbs of affirmation (‘absolutely’,‘seemingly’) that have an influence on the strength of attitude of the related words throughcoefficients (from 0.0 to 2.0)
• negation words (‘never’, ‘nothing’), adverbs of doubt (‘scarcely’, ‘hardly’) and adverbs offalseness (‘wrongly’) that reverse the polarity of related statement• prepositions (‘without’, ‘despite’) and condition operators (‘if’, ‘even though’) thatneutralize the attitude of related words

Basedd on theBh thoroughhh analysis
l i off 270 first-levelfi l l classes
loff VerbNet
V bN andd
their members, 73 verb classes (1) were found useful for the task of attitude
analysis, and (2) were further classified into 22 classes differentiated by therole that members play in attitude analysis.
For each of our verb classes, we developed set of rules that are applied toattitude analysis on the phrase/clause-level.
EXAMPLES:“Object-centered (oriented) emotional state” (subject is ignored):
S & V+(‘admires’) & O-(‘mafia leader’) =>interior: (verb valence dominance, V_score) =>‘POS aff’;
exterior:
i (verb( b valencelreversal,l max(V_score,O_score))(VO)) => ‘NEG jud’.
j d’“Subject-driven change in emotional state (trans.)” (object is ignored):(4) “functional” words:• intensifying adjectives (‘rising’, ‘rapidly-growing’), nouns (‘increase’, ‘up-tick’), and verbs(‘to grow’, ‘to rocket’), which increase the strength of attitude of related words• reversing adjectives (‘reduced’), nouns (‘termination’, ‘reduction’), and verbs (‘todecrease’, ‘to limit’, ‘to diminish’), which reverse the prior polarity of related words(5) modal operators, which indicate a degree of person’s belief in the truthof the proposition (confidence level):• modal verbs (conf(‘may’) = 0.27, conf(‘would’) = 0.8)
• modal adverbs (conf(‘vaguely’) = 0.17, conf(‘arguably’) = 0.63, conf(‘veritably’) = 1.0)

S-(‘Fatal consequences of GM food intake’) & V-(‘frighten’) & O(‘me’) =>interior: ‘NEG aff’; exterior: ‘NEG app’.

“Negative judgment”:
S(‘He’) & V-(‘blamed’) & O+(‘innocent person’) => interior: ‘NEG jud’; exterior: ‘NEG jud’.

“Adverse (unfavorable) attitude”:
S(‘They’) & [V neg. reinforcement](‘prevented’) & O-(‘the spread of disease’) => ‘POS app’;O_score^.

“Communication indicator/reinforcement of attitude” and“Termination
Termination of activityactivity”::

CCompositionality
itilit Principle
P i i lThe attitudinal meaning of a sentence is determined by composing thepieces that correspond to lexical units or other linguistic constituent types
governed at various grammatical levels by the rules ofpolarity reversal: ‘never’ & POS(‘succeed’) => NEG(‘never succeed’)

S(‘Max’) & [V neg. reinforcement, confidence:0.2](‘doubt’) & PP-{‘that’ S+(‘his good fortune’)& [V termination](‘will ever end’)} => ‘POS app’; PP_score^; confidence:0.2.

@AM Interface and Evaluation Results
The evaluation of our method
on 1000 sentences, which
describe personal experiences(Experience
Project:www.experienceproject.com),ij t)
showed promising results:
average accuracy on finegrained level (14 categories)
was 62%, on middle level (7categories) – 71%, and on top
level (3 categories) – 88%.

‘scarcely’ & POS(‘relax’) => NEG(‘scarcely relax’)‘reduced’ & POS(‘enthusiasm’) => NEG(‘reduced enthusiasm’))aggregation (fusion): POS(‘beautiful’) & NEG(‘fight’) => POS-neg(‘beautiful fight’)
NEG(‘shamelessly’) & POS(‘celebrate’) => NEG-pos(‘shamelessly celebrate’)propagation:ti
PROP POS(‘ admire’)
PROP-POS(‘tod i ’) & ‘his
‘hi behaviour’b h i ’ => POS(‘hisPOS(‘hi behaviour’)b h i ’)
‘Mr. X’ & TRANS(‘supports’) & NEG(‘crime business’) => NEG(‘Mr. X’))domination: NEG(‘to deceive’) & POS(‘hopes’) => NEG(‘to deceive hopes’)‘NEG(It was hard to climb a mountain all night long), but POS(a magnificent
view rewarded the traveler at the morning).’ => POS(whole sentence)neutralization: ‘despite’ & NEG(‘worries’) => NEUT(‘despite worries’)
intensification: Pos_score(‘extremely happy’) > Pos_score(‘happy’))
Neg_score(‘sad’) < Neg_score(‘sadder’) < Neg_score (‘saddest’)).
CONTACT: Helmut Prendinger, Associate Professor

TEL : +81-3-4212-2650

FAX : +81-3-3556-1916

Email : helmut@nii.ac.jp