Transcript

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Affective Transitions!

Studying Affective Transitions

Experience Project mood status corpus

alivesleepy

stressedoptimistic

boredblah

cheerfulconfusedamusedannoyedanxioushopefullonelytiredsad

exciteddepressed

calmhornyhappy

253232631626777285692864329119292352985031609332203499837504

515905209752975

6303565614

7634477209

89344

Moods Corpus 2 million posts

Emotion Transitions lo

g-sc

ale

CTP

(a,b

)

bewilderedartisticcuriousbouncychill

distressed

disappointedexcited

bewilderedcrushed

chill

chipperlazybitchybored

chipperblissfulbouncyenergeticflirty

numb

lonelydevastated

angry

melancholy

loved

amazing

goodexcitedamazed

optimistic

disappointed

peacefulblessed

determined

lonelycrusheddevastatedokay

melancholy

soreenergeticexcitedpeacefulrelaxed

busy

exhausteddistresseddrainednumb

awakesore

lazybusy

exhausted

worried

devastatednumb

okay

scared

amused anxious blah cheerful depressed happy hopeful sad satisfied stressed tired upset

0.03

0.04

0.06

0.1

Emotion Transitions

Worried

Hopeful

Blessed

Social Forces!

Studying Social Forces

Review Ratings

Rating

Reviews

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

0.3K

1.3K

2.5K

3.3K

4.8K

7.9K

negative(18% of reviews)

positive(25% of reviews)

neutral(58% of reviews)

2.9 million reviews

Review Sequence Transitions

negative

neutral

positive

positive neutral negative

0.07

0.21

0.51

0.4

0.67

0.44

0.53

0.12

0.04

(a) All product sequences

negative

neutral

positive

positive neutral negative

0.27

0.27

0.27

0.56

0.56

0.56

0.17

0.17

0.17

negative

neutral

positive

positive neutral negative

0.05

0.11

0.35

0.3

0.6

0.47

0.66

0.28

0.18

Review Sequence Transitions

negative

neutral

positive

positive neutral negative

0.07

0.21

0.51

0.4

0.67

0.44

0.53

0.12

0.04

(a) All product sequences (b) High-variance sequences (c) Randomized sequences

Classification Experiments

Conditional Random Field (CRF)

vs.

MaxEnt

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In an hour from now…

I actually kind of liked it.

Bla bla … sentiment … bla bla bla … networks …

Dude, that was even more boring than his

gray shirt, eh?!

Yeah right. Great talk… He didn’t even

talk about deep learning.

Modeling person-to-person opinions:The NLP approach

Bla bla … sentiment … bla bla bla … networks …

Dude, that was even more boring than his

gray shirt, eh?!

I actually kind of liked it.Yeah right. Great

talk… He didn’t even talk about deep

learning.

Modeling person-to-person opinions:The social-network–analysis approach

Social balance theory

“The enemy of my enemy is my friend”

“The enemy of my friend

is my enemy”

“The friend of my friend is my friend”

Modeling person-to-person opinions:A unified view

++

–“The friend

of my enemy is my enemy”

– Yeah right. Great talk… He didn’t even talk

about deep learning.

Social balance theory

Modeling person-to-person opinions:A unified view

+“The friend of my friend is my friend”

++

The talk was amazing!

I couldn’t attend — I was stuck on

the Autobahn.

Social balance theory

ModelRepresent social network as a signed graph:

G = (V,E, p, x)}

fully observed

partially observed

x 2 {0, 1}|E|p 2 [0, 1]|E|

text-based sentiment predictions

edge signs 0 = – 1 = +

Task: Infer unobserved portion of x“Boring!”

v 2 Ve1 2 E

x1 = 0p1 = 0.04

x4 = ?p4 = 0.55

“Okay.”

We want to infer unobserved portion of such that we1. agree with text-based sentiment predictions, i.e., 2. get triangles in line with social theories

Trade-off:

Objective functionx

x ⇡ p

T

} }

Cost for deviating from text-based sent. prediction

Cost of triangle typext 2 {0, 1}3

EDGE COST TRIANGLECOST

x

⇤ = argminx2{0,1}|E|

X

e2E

|xe

� p

e

|+X

t2T

d(xt

)

HL-MRF (Broecheler et al., 2010)• Markov random field (MRF) with continuous variables• Potentials are sums of hinge-loss terms of linear

functions of variables.• Relaxed objective function equal to original

formulation when is binary, i.e., ,• but interpolates over continuous domain .• Objective function convex.• Efficiently solvable.

Relaxation as hinge-loss Markov random field (HL-MRF)

x 2 {0, 1}|E|

[0, 1]|E|

x

. .

x

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Thank you!


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