chris potts: sentiment analysis in context

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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 Corpus2 million posts

  • Emotion Transitionslo

    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

  • 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 didnt 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 didnt even talk about deep

    learning.

  • Modeling person-to-person opinions:The social-networkanalysis 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 didnt 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 couldnt 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 xBoring!

    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|x

    e

    pe

    |+X

    t2Td(x

    t

    )

  • 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

  • Thank you!