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Interfacing WordNet-Affectwith OCC Model of Emotions
Alessandro ValituttiCarlo Strapparava
FBK-Irst - Istituto per la ricerca scientifica e tecnologica
Motivations
Affective analysis of text is a relatively newarea of research
Important for many NLP applications Opinion mining Market analysis Affective user interfaces E-learning environments
Overview of the talk
Brief outline of Wordnet-Affect The role of the subject for affective words A work in progress:A work in progress: Wordnet-Affect Wordnet-Affect andand
itsits integration withintegration with OCCOCC, an appraisal, an appraisalmodel of emotionsmodel of emotions
Affective lexical resources What an emotion is ?
Notoriously it is a difficult problem.Many approaches: facial expressions (Ekman), actiontendencies (Frijda), physiological activity (Ax), …
Emotions, of course, are not linguistic things However the most convenient access we have to
them is through the language
Ortony et al. (1987) introduced the problem
Some affective lexical resources
General Inquirer (Stone et al.) SentiWordNet (Esuli and Sebastiani) Affective Norms for English Words (ANEW)
(Bradley and Lang) WordNet Affect (Strapparava and Valitutti)
Affective semantic similarity All words can potentially convey affective
meaning Even those not directly related to emotions can
evoke pleasant or painful experiences Some of them are related to the individual
story But for many others the affective power is part
of the collective imagination (e.g. mum, ghost,war, …)
cfr. Ortony & Clore
C. Strapparava and A. Valitutti and O. Stock “The Affective Weight of Lexicon” Proceedings of LREC 2006
WordNet Affect
We built an affective lexical resource, essentialfor affective computing, computational humor,text analysis, etc.
It is a lexical repository of the direct affectivewords
The resource, named WordNet-Affect, startedfrom WordNet, through selection and labelingof synsets representing affective concepts.
WordNet
WordNet is an on-line lexical reference systemwhose design is inspired by psycholinguistictheories of human lexical memory
English nouns, verbs, adjectives and adverbsare organized into synonym sets (synsets),each representing one underlying lexicalconcept
IRST extensions: multilinguality and DomainLabels (WordNet Domains)
Analogy with WordNet domains In WordNet Domains each synset has been
annotated with a domain label (e.g. Sport,Medicine, Politics) selected form a setof 200 labels hierarchically organized
In WordNet-Affect we have an additionalhierarchy of affective domain labels(independent from the domain labels) withwhich the synsets representing affectiveconcepts are annotated
Affective domain labels Ortony distinguishes between emotional and affective-not-emotional
words Among affective-not-emotional words many categories from the
literature (e.g. Johnson-Laird and Oatley, 1988 Ortony et al., 1988)
a-label
emotion
affective
mood
trait
cognitive-state
physical-state
hedonic-signal
emotion-eliciting situation
emotional response
behavior
attitude
sensation
…. the hierarchy of the emotions…..
A-Labels and some examplesA-Label Examples of Synsets
EMOTION noun "anger#1", verb "fear#1"
MOOD noun "animosity#1", adjective "amiable#1"
TRAIT noun "aggressiveness#1", adjective "competitive#1"
COGNITIVE STATE noun "confusion#2", adjective "dazed#2"
PHYSICAL STATE noun "illness#1", adjective "all_in#1"
HEDONIC SIGNAL noun "hurt#3", noun "suffering#4"
EMOTION-ELICITING SITUATION noun "awkwardness#3", adjective "out_of_danger#1"
EMOTIONAL RESPONSE noun "cold_sweat#1", verb "tremble#2"
BEHAVIOUR noun "offense#1", adjective "inhibited#1"
ATTITUDE noun "intolerance#1", noun "defensive#1"
SENSATION noun "coldness#1", verb "feel#3"
Freely available (for research purposes) at http://wndomains.itc.it
Emotions of WN-affect
Specialization of the Emotional Hierarchy.For the present work we provide aspecialization of the a-label Emotion
Stative/Causative tagging.Concerning mainly the adjectivalinterpretation
Valence Tagging.Positive/Negative dimension
Emotional hierarchy
With respect to WN-Affect, we provided someadditional a-labels, hierarchically organizedstarting form the a-label Emotion
About 1637 words / 918 synsets
Stative/Causative tagging
An emotional adjective is called causative if itrefers to some emotion that is caused by themodified noun (e.g. “amusing movie”)
An emotional adjective is called stative if itrefers to some emotion owned or felt byentity denoted by the modified noun (e.g.“cheerful/happy boy”)
Valence tagging
Distinguishing synsets according to emotionalvalence
Positive emotions (joy#1, enthusiasm#1), Negative emotions (fear#1, horror#1), Ambiguous, when the valence depends on
the context (surprise#1) , Neutral, when the synset is considered
affective but not characterized by valence(indifference#1)
Corpus-based affective semantic similarity
An example of corpus-based application
We needed a technique for evaluating the affectiveweight of generic words
The mechanism is based on similarity betweengeneric terms and affective lexical concepts
We estimated term similarity from a large scalecorpus (BNC ~ 100 millions of words)
Latent Semantic Analysis => dimensionality reductionoperated by Singular Value Decomposition on theterm-by-documents matrix
Homogeneous representations
In the Latent Semantic Space, we canrepresent in a homogeneous way Words Texts Synsets
Each text (and synsets) can be represented inthe LSA space exploiting a variation of thepseudo-document methodology=> summing up the normalized LSA vectorsof all the terms contained in it
LSA spaced3
d1
d2
synset = w1+ w2 + w3
term = w1
Similarity: cosine among vectors
Affective synset representation
Thus an affective synset (and then anemotional category) can be represented in theLatent Semantic Space
We can compute a similarity measure amongterms and affective categories
Ex. the term “gift” is highly related (in BNC)with the emotional categories: Love (with positive valence) Compassion (with negative valence) Surprise (with ambiguous valence) Indifference (with neutral valence)
An example: university
Encouragementachievement
Devotionscholarship
Sympathyprofessor
Enthusiasmuniversity
Positive emotional categoryRelated emotional terms
Melancholyscholarship
Isolationstudy
Antipathyprofessor
Downheartednessuniversity
Negative emotional categoryRelated emotional terms
News titles
E.g. the affective weight of some news titles
protest#vAngerDead whale in Greenpeace protest
suffer#vSadnessRecord sales suffer steep decline
crash#vFearRomania: helicopter crash kills four people
pleasure#nJoyReview: `King Kong’ a giant pleasure
Word with highestaffective weightEmotionNews titles (Google-news)
Some resources and functionalities for dealingwith affective evaluative terms
An affective hierarchy as an extension ofWordNet-Affect lexical database, includingemotion, causative/stative and valencetagging
A semantic similarity mechanism acquired inan unsupervised way from a large corpus,providing relations among concepts andemotional categories
Summing up
WN-affect and OCC model
Role of the subject for indirect affectivewords
For example: help, revenge, victory arepositive or negative according to the role ofthe subject
The word victory can be used for expressingpride (related to the “winner”), but alsodisappointment (related to the “loser”)
WN-affect and OCC model
Another contextual information that can matter:temporal aspects
If victory is a future (desired) event in the futureprobably the expressed emotion is hope
If victory refers to a past event, a possible emotion issatisfaction
Taking into account appraisal theories=> a process of cognitive evaluation of perceivedconditions
WN-affect and OCC model
An appraisal model of emotions OCC (from Ortony, Clore and Collins, 1988) In OCC emotions are classified according to
some main categories such as events, objectsand actions, expressing possible cause ofemotions
Contextual information: different subject or roles in an actions as a future development: considering different
temporal conditions
WN-affect and OCC model
We rearrange OCC and we integrate it withthe affective hierarchy of WN-affect
We obtain a new organization of synsets witha set of additional labels called OCC-labels
Integrating WN-Affect and OCC
1. Rearrangement of OCC hierarchy: Valence inheritance
2. Mapping between WN-Affect categories andOCC emotion nodes
3. Insertion of OCC type nodes4. Annotation of synsets with new affective
labels The same synset can have more labels Emotion labels are associated to appraisal labels
OCC Emotions
FORTUNES-OF-OTHERS•HappyFor•Pity•Gloating•Resentment
ATTRACTION•Liking•Disliking
PROSPECT-BASED•Hope•FearsConfirmed•Disappointment•Fear•Satisfaction•Relief
ATTRIBUTION•Admiration•Pride•Reproach•Shame
WELL-BEING/ATTRIBUTION•Gratitude•Remorse•Gratification•Anger
WELL-BEING•Distress•Joy
ATTRACTION/ATTRIBUTION•Hate•Love
OCC Positive Emotions
FORTUNES-OF-OTHERS•HappyFor•Pity•Gloating•Resentment
ATTRACTION•Liking•Disliking
PROSPECT-BASED•Hope•FearsConfirmed•Disappointment•Fear•Satisfaction•Relief
ATTRIBUTION•Admiration•Pride•Reproach•Shame
WELL-BEING/ATTRIBUTION•Gratitude•Remorse•Gratification•Anger
WELL-BEING•Distress•Joy
ATTRACTION/ATTRIBUTION•Hate•Love
OCC Negative Emotions
FORTUNES-OF-OTHERS•HappyFor•Pity•Gloating•Resentment
ATTRACTION•Liking•Disliking
PROSPECT-BASED•Hope•FearsConfirmed•Disappointment•Fear•Satisfaction•Relief
ATTRIBUTION•Admiration•Pride•Reproach•Shame
WELL-BEING/ATTRIBUTION•Gratitude•Remorse•Gratification•Anger
WELL-BEING•Distress•Joy
ATTRACTION/ATTRIBUTION•Hate•Love
Emotions from EventsEVENTS
SIMPLE EXPECTEDNESS
EXPECTED UNEXPECTED
BEFOREAFTER
POSITIVE NEGATIVE
HOPE FEAR
POSITIVE NEGATIVE
POSITIVE-SURPRISE
NEGATIVE-SURPRISE
DISCONFIRMED
CONFIRMED
POSITIVE NEGATIVE POSITIVE NEGATIVE
SATISFACTION FEARSCONFIRMED
RELIEF FRUSTRATION
Emotions from ActionsEVENTS
ACTOR
ACTEE
POSITIVE NEGATIVE
GRATIFICATION
SHAME
POSITIVE NEGATIVE
ADMIRATIONREPROACH
OBSERVER
POSITIVE ACTION NEGATIVE ACTION
POSITIVE NEGATIVE NEGATIVE
GRATITUDE INGRATITUDE RESENTMENT
Actions,
Events
Objects
Examples
Emotions arising from the evaluation of actions
Emotions arising from the evaluation of“fortune/misfortune” by other people (observers)
Positive-Emotion Negative-Emotion Fortune happy-for envy Misfortune gloat pity
Positive-Emotion Negative-Emotion actor Pride Shame actee gratitude resentment observer admiration reproach
Behavior
A Sketch from the Annotation
Encouragement
Actor
Actee
COMPASSION ADMIRATION
PRIDE
GRATITUDE
POSITIVE HOPE
SATISFACTION
Trait
A Sketch from the Annotation
Ambition
Subject
Others
PRIDE
ENTHUSIASM
ENVY
ADMIRATION
Quality
A Sketch from the Annotation
Vanity
Subject
Others
PRIDE
DISLIKE
Reproach
Possible applications
WN-Affect-OCC could be exploited to performimprovements in sentiment analysis andaffect sensing
identification of the emotion-holder identification of the emotion-target OCC analysis of the emotion-target
“I need help” => sadness “Your help was useful” => gratitude
Future Work
Still a work in progress Extension of annotation to synsets with pos
“adjective”, “verb”, and “adverb” Not only subjectivity but also other appraisal features
(e.g. temporal features in the case of emotionselicited by expectedness of events)
Improvement of the synset annotation according todifferent sources of stereotypical knowledge (e.g.Latent Semantic Analysis)
Conclusions
Still a work in progress Development of WN-Affect-OCC as extension of WordNet-Affect
with the integration of OCC model of emotions. Tagging of (indirect affective) words referring to cause of
emotions (through the process of cognitive appraisal) Introduction of appraisal variables that can be disambiguated
from the analysis of textual context (e.g. subjectivity:actor/actee/others)
Improvement of the emotional hierarchy and distinctionaccording to the valence values