1.
Interac(ve
Visual
Analysis
of
Human
Emo(ons
Sen(ment,
Mood,
and
Emo(onal
DNA
Michelle
X.
Zhou
mzhou@juji-­‐inc.com

2.
Modeling
Human
Emo(ons
Basic
Emo*ons:
Moment
of
feelings
“I
was
so
happy”
“I
am
very
sad”
Human
emo2ons
are
complex
and
mul2-­‐dimensional
[Frijda
1993;
Ekman
2007]

3.
Modeling
Human
Emo(ons
Human
emo2ons
are
complex
and
mul2-­‐dimensional
“The
app
is
just
awesome”
“The
app
is
dreadful
and
hard
to
use”
Sen*ment:
A
feeling
toward
a
situa(on

4.
Modeling
Human
Emo(ons
Human
emo2ons
are
complex
and
mul2-­‐dimensional
“Recently
I
have
been
in
a
good
mood”
Mood:
Longer-­‐las(ng
feelings
over
a
period
of
(me
[Frijda
1993;
Ekman
2007]

5.
Modeling
Human
Emo(ons
– Emo(onal
outlook
“I
am
very
op2mis2c”
– Emo(onal
vola(lity
“I
am
quite
calm”
– Emo(onal
resilience
Human
emo2ons
are
complex
and
mul2-­‐dimensional
Emo*onal
DNA:
Unique
emo(onal
paXerns
and
characteris(cs
[Davidson
&
Begley
2012]

6.
Modeling
Human
Emo(ons
Basic
Emo*ons
Sen*ment
Mood
Emo*onal
DNA
Thing
Outlook
Vola(lity
Resilience

7.
PEARL:
Interac(ve
Visual
Analy(cs
of
Personal
Emo(onal
Style
from
Social
Media
[Zhao
et
al.
VAST
2014]

8.
Personal
Emo(onal
Style
from
Social
Media
• What
are
your
basic
emo(ons
conveyed
by
your
current
posts?
• What
is
your
mood
over
the
(me
period
X?
• What
is
your
overall
emo(onal
style?

9.
Live
Demo
hXp://www.cs.toronto.edu/~jianzhao/webapp/Pearl/pearl.html

10.
Emo(onal
Psychology:
Types
of
Emo(on
• 8
basic
emo(ons
• Complex
emo(ons
composed
of
basic
ones
[Plutchik
2001]

11.
Emo(onal
Psychology:
Dimensions
of
Emo(on
• Valence
– Posi(vity
• Arousal
– Intensity
• Dominance
– In-­‐control
Valence
Arousal
Happy
Rage
Dominance
Fear
Anger
Sad
[Mehrabian
1980]

12.
Analyze
Personal
Emo(ons
from
Text:
Detect
Basic
Emo(ons
• Lexical
based
approach
• Aggregate
the
amount
of
evidence
+
VAD
scores
Valence
Arousal
Dominance
Anger
v1
a1
d1
An*cipa*on
v2
a2
d2
Disgust
.
.
.
Fear
.
.
.
Joy
.
.
.
Sadness
.
.
.
Surprise
.
.
.
Trust
v8
a8
d8
E(t):
[M4x8(t)]
[Calvo
&
Kim
2013]

13.
Analyze
Personal
Emo(ons
from
Text:
Infer
Mood
Constraint-­‐based
co-­‐clustering
to
aggregate
basic
emo(ons
to
infer
mood
– Temporal
proximity
– Emo(on
proximity
– Seman(c
proximity
[Pan
et
al.
IUI
2013]

14.
Visually
Iden(fying
Emo(onal
Style
Valence
Emo(on
Bubble
+
Band

15.
Visually
Iden(fying
Emo(onal
Style
Resilience
Outlook
Vola(lity

16.
Evalua(on
• How
good
is
our
emo(on
analysis?
• How
useful
is
our
analysis?
– Discovery
of
personal
emo(onal
style
– Discovery
of
others’
emo(onal
style

17.
Ground
Truth
vs.
Derived
Emo(ons
10
people
over
1
month
of
tweets,
~60
segments
Cohen’s
Kappa
indica(ng
the
agreement
between
ground
truth
and
derived
emo(ons.

18.
Discovering
Personal
Emo(ons
• 6
people,
each
analyzed
over
2-­‐
year
of
own
tweets
(600-­‐4000)
• Self
awareness
• Valida(on
Valence

19.
Discovering
Others’
Emo(ons
• 50
Turkers,
44
valid
results
• 10
emo(on
analysis
tasks
on
user
data
w/
ground
truth
“in
which
2me
period,
the
person
is
most
emo2onally
submissive?”

20.
OpinionBlocks:
Crowd-­‐Powered,
Self-­‐Improving
Visual
Analy(cs
of
Opinion
Text
[Hu
et
al.
INTERACT
2013]

21.
The
Challenge
• User
challenge
– Much
content
and
liXle
(me
• 18000
reviews
for
Kindle
Fire
• Research
challenge
– Sen(ment
analysis
is
hard
– Feature-­‐based
sen(ment
analysis
is
harder
[Liu
2012]

22.
OpinionBlocks

23.
Crowd-­‐Powered,
Self-­‐Enhancing
Visual
Sen(ment
Analysis

24.
How
Well
A
Crowd
Can
Help
88.8%
users
made
right
correc(ons
95%
users
were
willing
to
correct
machine
mistakes

25.
Quality
of
Improvements
• Two
crowds
performed
same
set
of
tasks
– First
crowd
made
system
improvements
– Second
crowd
used
the
improvements
• A
set
of
non-­‐trivial
tasks
• Significantly
improved
task
comple(on
(me

26.
Remaining
Challenges
• Limita(ons
in
current
emo(on
analysis
– Lexical
approach
– Full
NLP
• Visual
summariza(on
of
analy(c
results
– Need
to
take
into
account
of
analy(c
confidence/uncertainty
• Real-­‐(me,
incremental
emo(on
analysis
• Modeling
paXerns
of
emo(ons
– Temporal
– Individual
differences

27.
Acknowledgement
• Jian
Zhao
• Mengdie
Hu
• Liang
Gou
• Huahai
Yang
• Yunyao
Li
• Fei
Wang
• Eben
Haber

28.
Call
for
Submissions
hXp://(is.acm.org/

29.
References
• J.
Zhao,
L.
Gou,
F.
Wang,
M.X.
Zhou:
PEARL:
An
interac(ve
visual
analy(c
tool
for
understanding
personal
emo(on
style
derived
from
social
media.IEEE
VAST
2014:
203-­‐212.
• M.Hu,
H.
Yang,
M.X.
Zhou,
L.
Gou,
Y.
Li,
E.
Haber:
OpinionBlock:
A
Crowd-­‐Powered,
Self-­‐
improving
Interac(ve
Visual
Analy(c
System
for
Understanding
Opinion
Text.
INTERACT
(2)
2013:
116-­‐134.
• S.
Pan,
M.X.
Zhou,
Y.
Song,
W.
Qian,
F.
Wang,
S.
Liu:
Op(mizing
temporal
topic
segmenta(on
for
intelligent
text
visualiza(on.
IUI
2013:
339-­‐350.
• N.
H.
Frijda.
Moods,
emo(on
episodes
and
emo(ons.
In
M.
Lewis
and
J.
M.
Haviland,
editors,
Handbook
of
Emo(ons,
pages
381–403.
Guilford
Press,
1993.
• P.
Ekman.
Emo(ons
Revealed.
Holt
Paperbacks,
2
edi(on,
2007.
• R.
J.
Davidson
and
S.
Begley.
The
Emo(onal
Life
of
Your
Brain:
How
Its
Unique
PaXerns
Affect
the
Way
You
Think,
Feel,
and
Live–and
How
You
Can
Change
Them.
Hudson
Street
Press,
2012.
• B.
Liu.
Sen(ment
Analysis
and
Opinion
Mining.
Morgan
&
Claypool
Publishers,
2012.
• A.
Mehrabian.
Basic
Dimensions
for
a
General
Psychological
Theory.
Oelgeschlager,
Gunn
&
Hain
Inc.,
1980.
• R.
A.
Calvo
and
S.
Mac
Kim.
Emo(ons
in
text:
Dimensional
and
categorical
models.
Computa(onal
Intelligence,
29(3):527–543,
2013.
• R.
Plutchik.
The
nature
of
emo(ons.
American
Scien(st,
89(4):344,
2001.