Twitter Sentiment Analysis - final - no personal

ADVISORYPARENTAL

EXPL IC IT CONTENT

What Makes a Good Model?

Team Grant

dammnit I'm lit. &dammnit I kn0 ders b0ut2be kiLLer traFFic! & ya d0nt even kn0 h0w haPPy I am dats its back2sch00l POSITIVE Or, Twitter Sentiment Analysis:

using models to classify tweets

so you don’t have to

a good model is

1. Valuable2. Accurate3. Sophisticated4. Agile

1a good model is valuable

$20M=

2a good model is accurate

(and the limits of that accuracy understood)

77%*Performance on 20% hold-out sample.It’s a hell of a lot better on the training sample.(Obviously.)

*

*2%better than

55%hosted sentiment classifier

75%trained sentiment classifier

39% 11%

12% 38%

of the 23% the model got wrong…

model error 41%

neutral 30%

human error 15%

other 13%

You ever have those days where you feel like you = FAIL. Yeah. It's one of those days.

Model + / Human -

UP is intense! i cried and laughed

Model - / Human +

Sorry, typo - Environmentalism.

Model - / Human +

@Zee It's good, but buggy like a motherfucker.

Model + / Human -I really hate twitter... i don't know what i'm doing here

Model - / Human +

so tierd could drop DEAD x

Model - / Human +ActiveRecord::HasManyThroughSourceAssociationMacroError: Invalid source reflection macro :has_one for has_many -> http://bit.ly/135UWH

Model + / Human -

@Dichenlachman I like that you abbreviated bathrooms to b'throoms when b'throoms is the same no. of letters as bathrooms... Bathrooms

Model - / Human +

bootstrapped hold-out performance

0.75 0.76 0.77 0.78 0.79

μ0.768

-1 σ 0.761

-2 σ 0.755

1 σ 0.775

2 σ 0.781

3 σ 0.788

3a good model is sophisticated

(but not too sophisticated)

classification process

raw tweets

NLP & features

model specifica

tiontraining analysis

made it hurt like a motherfucker fuck my life & i am not that short & your tall & i did grow some balls & date night tonight htp bit ly/nos

MADD-E. it hurt like a MOTHERFUCKER fuck my life & I am not that short & yr tall & i did grow some balls & date night tonight!1! http://bit.ly/Nos9D

1 raw tweet

2

5 vectorize [ 0 0 1 0 0 … 0 0 1 0 0 1 ] 6 model

MADD-E. it huuuurt like a MOTHERFUCKER fml & i’m not that short & yr tall & i did grow some balls & date night tonight!1! http://bit.ly/Nos9D

3

expand contractionssocial media lexiconcorrected XMLrepeat replace

spellcheckremove punctuationremove numbersall lowercase

4 uni-gramsbi-grams { made, it, made it, … }

why didn’t we do other cool NLP stuff?

twee

ts

what w

e did

engli

sh o

nly

rem

ove

Twitter

sym

bols

rem

ove

stop

words

stem

0.74

0.75

0.76

0.77

0.78accuracy

why didn’t we do other cool NLP stuff?

twee

ts

what w

e did

engli

sh o

nly

rem

ove

Twitter

sym

bols

rem

ove

stop

words

stem

0.74

0.75

0.76

0.77

0.78accuracy

raw

spellcheck

normalize case

stem / lemmatize

why does that happen?

LOVIN’

LOVING

loving

love

LOVIN LOVING

Loving loving

love loved

raw

spellcheck

normalize case

stem / lemmatize

why does that happen?

LOVIN’

LOVING

loving

love

fewerdimensions

(good)

lessinformation

(bad)

team Grant model specification

linea

r SVM

naïve

Bay

es (S

KL)

naïve

Bay

es (N

LTK)

rand

om fo

rests

(SKL)

optim

ized

non-

linea

r SVM

naïve

Bay

es (B

ig D

ata)

neur

al n

etwor

ks

decis

ion

tree

(SKL)

decis

ion

tree

(NLT

K)

VADER clas

sifie

r

TextB

lob

Patte

rnAna

lyzer

KNN

decis

ion

tree

(NLT

K, lexic

on)

decis

ion

tree

(NLT

K, em

otico

ns)

0.45

0.50

0.55

0.60

0.65

0.70

0.75

analysis

linea

r SVM

naïve

Bay

es (S

KL)

naïve

Bay

es (N

LTK)

rand

om fo

rests

(SKL)

optim

ized

non-

linea

r SVM

naïve

Bay

es (B

ig D

ata)

neur

al n

etwor

ks

decis

ion

tree

(SKL)

decis

ion

tree

(NLT

K)

VADER clas

sifie

r

TextB

lob

Patte

rnAna

lyzer

KNN

decis

ion

tree

(NLT

K, lexic

on)

decis

ion

tree

(NLT

K, em

otico

ns)

0.45

0.50

0.55

0.60

0.65

0.70

0.75

analysis

linea

r SVM

naïve

Bay

es (S

KL)

naïve

Bay

es (N

LTK)

rand

om fo

rests

(SKL)

optim

ized

non-

linea

r SVM

naïve

Bay

es (B

ig D

ata)

neur

al n

etwor

ks

decis

ion

tree

(SKL)

decis

ion

tree

(NLT

K)

VADER clas

sifie

r

TextB

lob

Patte

rnAna

lyzer

KNN

decis

ion

tree

(NLT

K, lexic

on)

decis

ion

tree

(NLT

K, em

otico

ns)

0.45

0.50

0.55

0.60

0.65

0.70

0.75

analysis

how do they work?

linear SVM

naïve Bayes

random forest

consensus

76%

76%

74%

DEMOCRACY! 77%

4a good model is agile

1. genetically diverse

2. ensemble can handle more libraries / classifiers

3. modular designa) NLPb) feature detectionc) models

4. sequential checks

5. quick enough to classify the firehose

6. easily incorporate new cases for re-training

?