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Computational Narrativeand what we can learn from narratology
Jessica Ouyang
Columbia University
September 2015
1/1
Introduction
storytelling is a fundamental and universal form of communication
I how are narratives different from non-narratives?I what do narratives tell us about authors and audiences?I can we generate narratives automatically?
2/1
Outline
where did we come from?I literary and linguistic studies of narrative
where are we now?I the current state of computational narrative research
where are we going?I how emotion detection can and should help us improve
computational narrative
3/1
NarratologyStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Propp (1928)
I there is a limited set offunctions that are thebuilding blocks of a fairy tale
I the sequence of functions isalways the same in everyfairy tale
some functions are more specificthan others
not all functions are equallyimportant
Vladimir Propp (1968). Morphology of the Folktale.4/1
NarratologyStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Propp (1928)
I there is a limited set offunctions that are thebuilding blocks of a fairy tale
I the sequence of functions isalways the same in everyfairy tale
some functions are more specificthan others
not all functions are equallyimportant
Vladimir Propp (1968). Morphology of the Folktale.4/1
NarratologyStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Todorov (1969)
I the structural approach tonarrative seeks an abstracttheory of the structure ofliterary discourse such thatall existing works areparticular realized instancesof that structure
I the minimal complete plot isa shift from one equilibirumstate to another
T. Todorov (1969). Structural analysis of narrative.5/1
NarratologyStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Barthes (1975)
I there must exist a commonnarrative structure,otherwise narratives are justrandom sequences of events
I we can reliably produce andrecognize narratives, so theycannot be random
R. Barthes (1975). An introduction to the structural analysis of narrative.6/1
NarratologyStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Barthes (1975)three levels of analysis
I functions
I charactersI narration
functions↙ ↘
events states↙ ↘
causal temporal
what are the decision points of anarrative?
R. Barthes (1975). An introduction to the structural analysis of narrative.7/1
NarratologyStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Barthes (1975)three levels of analysis
I functionsI characters
I narration
characters are defined by actionsI have perspectives on
sequences of actionsI participate in relationshipsI form subject/object,
giver/recipient, andassistant/opposer pairs
R. Barthes (1975). An introduction to the structural analysis of narrative.7/1
NarratologyStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Barthes (1975)three levels of analysis
I functionsI charactersI narration
the discourse of the narrativeI who is the narrator?
I the authorI an omniscient observerI a character
I who is the reader?
R. Barthes (1975). An introduction to the structural analysis of narrative.7/1
NarratologyStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
the story so far –I narrative has two parts
I surface form(narration/discourse)
I deep structure(functions/characters)
I every existing narrative is arealization of a deepstructure
I eg. Hamlet and The LionKing are two realizationsof the same deepstructure
this is the structuralist position
8/1
NarratologyStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
but wait –I even if the deep structure is
the same, the surfacerealization is very different
I even if the surfacerealization is the same, thereader’s interpretation canbe very different
I L. Bohannan (1966).Shakespeare in the Bush.
this is the contextualist position
9/1
NarratologyStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Smith (1980)
I narratives are not juststructures, but also acts
I a narrative has a narratorand reader, both of whommust be interested in thenarrative
I what does the narratorwant to convey?
I what does the reader takeaway from the narrative?
B. H. Smith (1980). Narrative versions, narrative theories.10/1
NarratologyStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Polanyi (1981)
I a narrative must be tellableI the durative-descriptive
structure shows thesociocultural context
I evaluation devices indicateimportant material thatforms an adequateparaphrase of the story
the surface realizationindicates the teller’s intendeddeep structure
L. Polanyi (1981). What stories can tell us about their teller’s world.11/1
NarratologyStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Polanyi (1981)
I a narrative must be tellableI the durative-descriptive
structure shows thesociocultural context
I evaluation devices indicateimportant material thatforms an adequateparaphrase of the story
the surface realizationindicates the teller’s intendeddeep structure
L. Polanyi (1981). What stories can tell us about their teller’s world.11/1
NarratologyStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
the lay of the land –
StructuralismI literatureI functions and actions
ContextualismI personal narrativeI discourse and context
12/1
NarratologyStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Chatman (1990)
I contextualists assume a realauthor/real audiencerelationship, which does notapply to literature
the theory might not apply toliterature, but this does not makeit less true in its intended domain
S. Chatman(1990). What can we learn from contextualist narratology?13/1
NarratologyStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Chatman (1990)
I there is less variety andinnovation of the form byamateur storytellers than byliterary authors
this makes amateur stories moresuitable for defining a generalstructure for narrative
S. Chatman(1990). What can we learn from contextualist narratology?13/1
NarratologyStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Chatman (1990)
I personal narratives do notdistinguish between thesurface realization and thedeep structure
this makes them perfect forstudying deep structure
S. Chatman(1990). What can we learn from contextualist narratology?13/1
NarratologyStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Labov (2013)abstract
contextualistorientation
structuralistcomplicating action
structuralistmost reportable event
contextualistevaluation
contextualistresolution
structuralistcoda
contextualistW. Labov (2013). The Language of Life and Death.
14/1
OutlineStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
NarratologyI structuralism: literature,
functions, character actionsI contextualism: personal
narrative, discourse, context
Computational NarrativeI coming up next
Emotion DetectionI later
15/1
Computational NarrativeStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Lehnert (1981)plot units
I vertices: affect statesI edges: causal links
–
M
m
⇒ Proppian functions
W. Lehnert (1981). Plot units and narrative summarization.16/1
Computational NarrativeStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Lehnert (1981)plot units
I vertices: affect statesI edges: causal links
–
M
m
⇒ Proppian functions
W. Lehnert (1981). Plot units and narrative summarization.16/1
Computational NarrativeStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Elson (2012)Story Intention Graph (SIG)
I three layers of annotationI textual ⇐ narrationI timeline ⇐ functionI interpretive⇐ character
I annotators felt there weremultiple interpretations forsome narratives
⇒ Barthes’s layers of analysis⇒ Contextualism
D. Elson (2012). DramaBank: annotating agency in narrative discourse.17/1
Computational NarrativeStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Elson (2012)Story Intention Graph (SIG)
I three layers of annotationI textual ⇐ narrationI timeline ⇐ functionI interpretive⇐ character
I annotators felt there weremultiple interpretations forsome narratives
⇒ Barthes’s layers of analysis⇒ Contextualism
D. Elson (2012). DramaBank: annotating agency in narrative discourse.17/1
Computational NarrativeStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Halpin, Moore, and Robertson(2004)
I 103 stories rewritten bychildren
I does the pupil understandthe point of the story andemphasize important linksand details?
⇐
I extract and compare theevents of the rewritten storywith exemplar
⇐
⇒ contextualist question⇒ structuralist answer
H. Halpin, J. D. Moore, and J. Robertson (2004). Automatic analysis ofplot for story rewriting.
18/1
Computational NarrativeStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Halpin, Moore, and Robertson(2004)
I 103 stories rewritten bychildren
I does the pupil understandthe point of the story andemphasize important linksand details? ⇐
I extract and compare theevents of the rewritten storywith exemplar
⇐
⇒ contextualist question
⇒ structuralist answer
H. Halpin, J. D. Moore, and J. Robertson (2004). Automatic analysis ofplot for story rewriting.
18/1
Computational NarrativeStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Halpin, Moore, and Robertson(2004)
I 103 stories rewritten bychildren
I does the pupil understandthe point of the story andemphasize important linksand details? ⇐
I extract and compare theevents of the rewritten storywith exemplar ⇐
⇒ contextualist question⇒ structuralist answer
H. Halpin, J. D. Moore, and J. Robertson (2004). Automatic analysis ofplot for story rewriting.
18/1
Computational NarrativeStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Chambers and Jurafsky (2008)narrative chain
I partially ordered set ofevents with a commonprotagonist
⇒ Barthes: characters aredefined by their actions
system average rank
verb co-occurence 1826protagonist 1160
but is this good performance?how to interpret these numbers?
N. Chambers and D. Jurafsky (2008). Unsupervised learning of narrativeevent chains.19/1
Computational NarrativeStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Chambers and Jurafsky (2008)narrative chain
I partially ordered set ofevents with a commonprotagonist
⇒ Barthes: characters aredefined by their actions
system average rank
verb co-occurence 1826protagonist 1160
but is this good performance?how to interpret these numbers?
N. Chambers and D. Jurafsky (2008). Unsupervised learning of narrativeevent chains.19/1
Computational NarrativeStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
McIntyre and Lapata (2009)
I construct DAG from eventchains (Chambers andJurafsky)
I generate narratives bywalking the DAG
The giant guards the child. Thechild rescues the son from thepower. The child begs the sonfor a pardon. The giant criesthat the son laughs the happinessout of death. The child hears ifthe happiness tells a story.
N. McIntyre and M. Lapata (2009). Learning to tell tales: a data-drivenapproach to story generation.
20/1
Computational NarrativeStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
McIntyre and Lapata (2009)
I construct DAG from eventchains (Chambers andJurafsky)
I generate narratives bywalking the DAG
The giant guards the child. Thechild rescues the son from thepower. The child begs the sonfor a pardon. The giant criesthat the son laughs the happinessout of death. The child hears ifthe happiness tells a story.
N. McIntyre and M. Lapata (2009). Learning to tell tales: a data-drivenapproach to story generation.
20/1
Computational NarrativeStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Reidl and Young (2010)
I Intent-Driven PartialOrder Causal Linkplanning problem
I for a plan to be complete, itmust not contain anycharacter actions that arenot part of a frame ofcommitment
The genie has a frighteningappearance. The genie appearsthreatening to Aladdin. Aladdinwants the genie to die.
M. Reidl and R. M. Young (2010). Narrative planning: balancing plot andcharacter.
21/1
Computational NarrativeStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Reidl and Young (2010)
I Intent-Driven PartialOrder Causal Linkplanning problem
I for a plan to be complete, itmust not contain anycharacter actions that arenot part of a frame ofcommitment
The genie has a frighteningappearance. The genie appearsthreatening to Aladdin. Aladdinwants the genie to die.
M. Reidl and R. M. Young (2010). Narrative planning: balancing plot andcharacter.
21/1
Computational Narrative
the story so far –
work data task approach
Lehnert – summarization plot unitsElson fables annotation SIGHaplin rewritten stories grading event similarityChambers Gigaword script extraction event chainMcIntyre fairy tales generation event chainReidl – generation IPOCL
structuralist ↗
22/1
Computational Narrative
the story so far –
work data task approach
Lehnert – summarization plot unitsElson fables annotation SIGHaplin rewritten stories grading event similarityChambers Gigaword script extraction event chainMcIntyre fairy tales generation event chainReidl – generation IPOCL
structuralist ↗
22/1
Computational NarrativeStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Montfort (2011)I interactive fiction system
that supports several modelsof narration
I reverse chronologicalorder
I flashback and flashforwardI tense generated
automatically based on aninternal state time ofnarration and the time ofthe event
N. Montfort (2011). Curveship’s automatic narrative style.23/1
Computational NarrativeStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Gordon and Swanson (2009)
I ICWSM 2009 Spinn3r datsetI annotated 5002 posts
I 240 stories (4.8%)I simple unigrams normalized
by frequencyI POS tags and title text
not statistically significantI created dataset of 960,098
stories (precision = 0.75)
⇒ narratives of personalexperience
A. S. Gordon and R. Swanson (2009). Identifying personal stories inmillions of weblog entries.
24/1
Computational NarrativeStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Gordon and Swanson (2009)
I ICWSM 2009 Spinn3r datsetI annotated 5002 posts
I 240 stories (4.8%)I simple unigrams normalized
by frequencyI POS tags and title text
not statistically significantI created dataset of 960,098
stories (precision = 0.75)
⇒ narratives of personalexperience
A. S. Gordon and R. Swanson (2009). Identifying personal stories inmillions of weblog entries.
24/1
Computational NarrativeStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Swanson et al (2014)
I identify orientation,complicating action, andevaluation
I several rounds of annotationto achieve agreement
I problematic clause typesI clauses containing of
multiple elementsI implied actionsI stative descriptions
resulting from local actionI subjective language
R. Swanson et al (2014). Identifying narrative clause types in personalstories.
25/1
Computational NarrativeStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
Sagae et al (2013)
I diegetic versus extradiegeticI subjective versus objective
task accuracy
six-way 58%binary subjectivity 78%
binary diegetic 81%
K. Sagae et al (2013). A data-driven approach for classification ofsubjectivity in personal narrative.
26/1
OutlineStructuralism
Propp
Todorov
Barthes
Chatman
Smith Polanyi
LabovContextualism
Lehnert
Elson
Chambers Halpin
Generation
McIntyre Reidl
MontfortGordon
Swanson Sagae
NarratologyI structuralism: literature,
functions, character actionsI contextualism: personal
narrative, discourse, context
Computational NarrativeI previous work has focused
on structuralist approachesI contextualist approaches are
needed
Emotion DetectionI coming up next
27/1
Emotion DetectionRubin Mishne
Tokuhisa
basic emotionsLiu
Chaumartin
Strapprava
Alm
Calix
Ulinski
Liu, Lieberman, and Selker (2003)I six basic emotions: happy, sad,
anger, fear, disgust, surpriseI Ekman (1993)
I evaluationI email client that illustrates
sentences with facesI faces generated by emotion
system rated more interactiveand intelligent than random faces
what was the rating scale?
H. Liu, H. Lieberman, and T. Selker (2003). A model of textual affectsensing using real-world knowledge.
28/1
Emotion DetectionRubin Mishne
Tokuhisa
basic emotionsLiu
Chaumartin
Strapprava
Alm
Calix
Ulinski
Liu, Lieberman, and Selker (2003)I six basic emotions: happy, sad,
anger, fear, disgust, surpriseI Ekman (1993)
I evaluationI email client that illustrates
sentences with facesI faces generated by emotion
system rated more interactiveand intelligent than random faces
what was the rating scale?
H. Liu, H. Lieberman, and T. Selker (2003). A model of textual affectsensing using real-world knowledge.
28/1
Emotion DetectionRubin Mishne
Tokuhisa
basic emotionsLiu
Chaumartin
Strapparava
Alm
Calix
Ulinski
Rubin, Stanton, and Liddy (2004)I Circumplex Theory of Affect
(Watson and Tellegen 1985)I positive affect axis (active, elated
v. drowsy, dull)I negative affect axis (distressed,
fearful v. calm, placid)
unintuitive axes
too much overlap among labels in thesame octant
V. Rubin, J. Stanton, and E. Liddy (2004). Discerning emotions in texts.29/1
Emotion DetectionRubin Mishne
Tokuhisa
basic emotionsLiu
Chaumartin
Strapparava
Alm
Calix
Ulinski
Mishne (2005)
I top 40 LiveJournal moods (eg.amused, excited, contemplative,sick, anxious, ecstatic)
too much subjectivity in self-reportedmoods
G. Mishne (2005). Experiments with mood classification in blog posts.30/1
Emotion DetectionRubin Mishne
Tokuhisa
basic emotionsLiu
Chuamartin
STrapparava
Alm
Calix
Ulinski
Tokuhisa, Inui, and Matsumoto(2008)
I happiness, pleasantness, relief, fear,sadness, disappointment,unpleasantness, loneliness, anxiety,anger
I fatal v. non-fatal errors
too much overlap among labels
R. Tokuhisa, K. Inui, and Y. Matsumoto (2008). Emotion classificationusing massive examples extracted from the web.
31/1
Emotion DetectionRubin Mishne
Tokuhisa
basic emotionsLiu
Chuamartin
STrapparava
Alm
Calix
Ulinski
Tokuhisa, Inui, and Matsumoto(2008)
I happiness, pleasantness, relief, fear,sadness, disappointment,unpleasantness, loneliness, anxiety,anger
I fatal v. non-fatal errors
too much overlap among labels
R. Tokuhisa, K. Inui, and Y. Matsumoto (2008). Emotion classificationusing massive examples extracted from the web.
31/1
Emotion DetectionRubin Mishne
Tokuhisa
basic emotionsLiu
Chaumartin
Strapparava
Alm
Calix
Ulinski
Chaumartin (2007)
I fine- v. coarse-grain evaluationI WordNet Affect and SentiWordNet
scores weighted by syntactic role ofword
I high accuracy, moderate precision,low recall
fine-grained control of emotion ispossible
F.-R. Chaumartin (2007). UPAR7: a knowledge-based system for headlinesentiment tagging.
32/1
Emotion DetectionRubin Mishne
Tokuhisa
basic emotionsLiu
Chaumartin
Strapparava
Alm
Calix
Ulinski
Strapparava and Mihalcea (2008)I LSA representation of WordNet
AffectI outperformed by Chaumartin on
fine-grained evaluationI performs best on coarse-grained
evaluationI strict WN keyword has best
precision (38.28)I synset augmented WN has best
recall (90.22)
C. Strapparava and R. Mihalcea (2008). Learning to identify emotions intext.
33/1
Emotion DetectionRubin Mishne
Tokuhisa
basic emotionsLiu
Chaumartin
Strapparava
Alm
Calix
Ulinski
Alm, Roth, and Sproat (2005)
I 185 children’s storiesI emotions in adjacent sentences can
affect the current sentenceI strongest feature group included
thematic story type
⇐
different types of stories should be toldin different ways
C. Alm, D. Roth, and R. Sproat (2005). Emotions from text: machinelearning for text-based emotion prediction.
34/1
Emotion DetectionRubin Mishne
Tokuhisa
basic emotionsLiu
Chaumartin
Strapparava
Alm
Calix
Ulinski
Alm, Roth, and Sproat (2005)
I 185 children’s storiesI emotions in adjacent sentences can
affect the current sentenceI strongest feature group included
thematic story type ⇐
different types of stories should be toldin different ways
C. Alm, D. Roth, and R. Sproat (2005). Emotions from text: machinelearning for text-based emotion prediction.
34/1
Emotion DetectionRubin Mishne
Tokuhisa
basic emotionsLiu
Chaumartin
Strapparava
Alm
Calix
Ulinski
Calix et al (2010)
I mutual information between wordsand emotions
I learned list of words outperformedhand-crafted word lists
I performance varied by author
authors express emotions in differentways
R. Calix, S. Mallepudi, B. Chen, and G. Knapp (2010). Emotionrecognition in text for 3-d facial expression rendering.
35/1
Emotion DetectionRubin Mishne
Tokuhisa
basic emotionsLiu
Chaumartin
Strapparava
Alm
Calix
Ulinski
Calix et al (2010)
I mutual information between wordsand emotions
I learned list of words outperformedhand-crafted word lists
I performance varied by author ⇐
authors express emotions in differentways
R. Calix, S. Mallepudi, B. Chen, and G. Knapp (2010). Emotionrecognition in text for 3-d facial expression rendering.
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Emotion DetectionRubin Mishne
Tokuhisa
basic emotionsLiu
Chaumartin
Strapparava
Alm
Calix
Ulinski
Ulinski, Soto, and Hirschberg (2012)
I 4443 LiveJournal entriesI 660 annotated WordsEye images
I literal descriptionI basic emotionI explanation for why that emotion
was chosenI LIWC classes, tf-idf of (word
n-gram, POS n-gram) pairsI cross-domain classification
M. Ulinski, V. Soto, and J. Hirschberg (2012). Finding emotion in imagedescriptions.
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Conclusion
structuralist narratology examines functions and character actionsin literature; contextualist narratology examines discourse andcontext in personal narrative
most work in computational narrative has focused on structuralistapproaches, but contextualist approaches should be explored,especially with the amount of online data now available
in particular, work on emotion in text can help in computationalnarrative tasks by uncovering authors’ intents and opinions
questions?
37/1
Conclusion
structuralist narratology examines functions and character actionsin literature; contextualist narratology examines discourse andcontext in personal narrative
most work in computational narrative has focused on structuralistapproaches, but contextualist approaches should be explored,especially with the amount of online data now available
in particular, work on emotion in text can help in computationalnarrative tasks by uncovering authors’ intents and opinions
questions?
37/1