9. hNp://www.amazon.com/
• Supervised
Latent
Dirichlet
AllocaRon[1]
– LDAと線形回帰のjoint
model
linear
regression
y = ˆT
ˆw
9
topic
distribuRon
=
feature
0
0.25
0.5
0.75
1
A
B
C
D
ˆ
基本手法
10. Supervised
LDA
• LDA+線形回帰やbag-‐of-‐words
+
Lassoよりも精度が改善
• jointで解くことでトピックに線形回帰の係数と対応した極性が
付与され,
単語分布もそれに応じる【評判のトピック】
10
Figure 1: (Left) A graphical model
representation of Supervised Latent
Dirichlet allocation. (Bottom) The
topics of a 10-topic sLDA model fit to
the movie review data of Section 3.
both
motion
simple
perfect
fascinating
power
complex
however
cinematography
screenplay
performances
pictures
effective
picture
his
their
character
many
while
performance
between
!30 !20 !10 0 10 20
! !! ! !! ! ! ! !
more
has
than
films
director
will
characters
one
from
there
which
who
much
what
awful
featuring
routine
dry
offered
charlie
paris
not
about
movie
all
would
they
its
have
like
you
was
just
some
out
bad
guys
watchable
its
not
one
movie
least
problem
unfortunately
supposed
worse
flat
dull
d Zd,n Wd,n
N
D
Kk
Yd η, σ2
By regressing the response on the empirical topic frequencies, we treat the response as non-
exchangeable with the words. The document (i.e., words and their topic assignments) is generated
first, under full word exchangeability; then, based on the document, the response variable is gen-
erated. In contrast, one could formulate a model in which y is regressed on the topic proportions
✓. This treats the response and all the words as jointly exchangeable. But as a practical matter,
of an unconstrained real-valued response. Then, in Section 2.3, w
sLDA, and explain how it handles diverse response types.
Focus now on the case y 2 R. Fix for a moment the model param
k a vector of term probabilities), the Dirichlet parameter ↵, and the
Under the sLDA model, each document and response arises from th
1. Draw topic proportions ✓ | ↵ ⇠ Dir(↵).
2. For each word
(a) Draw topic assignment zn | ✓ ⇠ Mult(✓).
(b) Draw word wn | zn, 1:K ⇠ Mult( zn ).
3. Draw response variable y | z1:N , ⌘, 2 ⇠ N ⌘> ¯z, 2 .
Here we define ¯z := (1/N)
PN
n=1 zn. The family of probability dis
generative process is depicted as a graphical model in Figure 1.
Notice the response comes from a normal linear model. The cova
observed) empirical frequencies of the topics in the document. The
frequencies constitute ⌘. Note that a linear model usually includes a
to adding a covariate that always equals one. Here, such a term is
nents of ¯z always sum to one.
2
Figure 1: (Left) A graphical model
representation of Supervised Latent
Dirichlet allocation. (Bottom) The
topics of a 10-topic sLDA model fit to
the movie review data of Section 3.
both
motion
simple
perfect
fascinating
power
complex
however
cinematography
screenplay
performances
pictures
effective
picture
his
their
character
many
while
performance
between
!30 !20 !10 0 10 20
! !! ! !! ! ! ! !
more
has
than
films
director
will
characters
one
from
there
which
who
much
what
awful
featuring
routine
dry
offered
charlie
paris
not
about
movie
all
would
they
its
have
like
you
was
just
some
out
bad
guys
watchable
its
not
one
movie
least
problem
unfortunately
supposed
worse
flat
dull
d Zd,n Wd,n
N
D
Kk
Yd η, σ2
14. • A
domain
can
contain
both
Domain-‐dependent
and
-‐independent
words.
BOOK
-‐
The
story
is
good
-‐
Too
small
leNer
-‐
Boring
magazine
-‐
Product
was
scratched
KITCHEN
-‐
The
toaster
doesn’t
work
-‐
The
knife
is
sturdy
-‐
This
dishcloth
is
easy
to
use
-‐
Customer
support
is
not
good
14
ドメイン依存問題
15. BOOK
-‐
The
story
is
good
-‐
Too
small
le?er
-‐
Boring
magazine
-‐
Product
was
scratched
KITCHEN
-‐
The
toaster
doesn’t
work
-‐
The
knife
is
sturdy
-‐
This
dishcloth
is
easy
to
use
-‐
Customer
support
is
not
good
15
• A
domain
can
contain
both
Domain-‐dependent
and
-‐independent
words.
ドメイン依存問題
16. BOOK
-‐
The
story
is
good
-‐
Too
small
le?er
-‐
Boring
magazine
-‐
Product
was
scratched
KITCHEN
-‐
The
toaster
doesn’t
work
-‐
The
knife
is
sturdy
-‐
This
dishcloth
is
easy
to
use
-‐
Customer
support
is
not
good
16
• A
domain
can
contain
both
Domain-‐dependent
and
-‐independent
words.
ドメイン依存問題
17. BOOK
-‐
The
story
is
good
-‐
Too
small
le?er
-‐
Boring
magazine
-‐
Product
was
scratched
KITCHEN
-‐
The
toaster
doesn’t
work
-‐
The
knife
is
sturdy
-‐
This
dishcloth
is
easy
to
use
-‐
Customer
support
is
not
good
17
• A
domain
can
contain
both
Domain-‐dependent
and
-‐independent
words.
ドメイン依存問題
• 製品のドメインで単語分布が異なるがsLDAではこれを考慮で
きない
– ドメインで使われる評価表現は異なるにも関わらず
• ドメイン適応と類似した対策を取りたい
18. BOOK
-‐
The
story
is
good
-‐
Too
small
le?er
-‐
Boring
magazine
-‐
Product
was
scratched
KITCHEN
-‐
The
toaster
doesn’t
work
-‐
The
knife
is
sturdy
-‐
This
dishcloth
is
easy
to
use
-‐
Customer
support
is
not
good
1.
Introducing
domain-‐dependent/independent
topics
into
sLDA
2.
Domain-‐Dependent/Independent
Topic
Switching
Model
(DDI-‐TSM)
Proposal
18
• A
domain
can
contain
both
Domain-‐dependent
and
-‐independent
words.
ドメイン依存問題
25. • Domain
Dependent/Independentは0/1の値をとるス
イッチの潜在変数で切り替える
switching
latent
variable
word
(observed)
topical
latent
variable
(same
as
LDA)
Z
0
music
Z
1
good
domain
dependent
topic
dist.
domain
independent
topic
dist.
Z
X
W
DD
DI
DD
DI
DD
DI
25
モデリングによるアプローチ
26. 26
K:
The
number
of
topics
in
sLDA
DDI-‐TSMは総トピック
数10~20で高速に
この精度を達成可能
評点回帰実験結果
30. Book−negative
Book−positive
DVD−negative
DVD−poisitive
Electronics−negative
Electronics−positive
Kitchen−negative
Kitchen−positive
weight that did not correspond to labels
bias
−16
−14
−12
−10
−8
−6
−4
−2
0
2
4 Domain-‐Dependent
Domain-‐Independent
weight
parameters
music video
great good best
better funny
interesting nice
like love wonderful
worst waste
poor boring
wrong terrible
like good best
funny interesting
-
product ipod work
player printer sony
phone battery
keyboard audio
button speaker
monitor memory
great good like
better excellent
perfect happy clear
product speaker work
sound phone player
software dvd radio
tv device printer
ipod computer
battery sony
button headphones
nothing waste never
didn’t cannot problem
disappointed doesn’t
good great
-
coffee water
machine filter
cooking food
glass steel
stainless ice
rice espresso
wine tea toaster
wonderful sturdy
sharp love great
good well easy
best better
product water coffee
steel tank kitchen
knives hot heat
maker design machine
work vacuum filter
don’t doesn’t
didn’t never problem
few broke less
disappointed poor
cheap nothing no
good great better nice
-
arthur harold
Electronics-‐posiRve
Kitchen-‐posiRve
30
コントロールされたトピックの単語分布
31. Book−negative
Book−positive
DVD−negative
DVD−poisitive
Electronics−negative
Electronics−positive
Kitchen−negative
Kitchen−positive
weight that did not correspond to labels
bias
−16
−14
−12
−10
−8
−6
−4
−2
0
2
4 Domain-‐Dependent
Domain-‐Independent
weight
parameters
Kitchen-‐negaRve
actor music cast
no never didn’t
worst waste
poor boring
wrong terrible
like good best
funny interesting
-
product speaker work
sound phone player
software dvd radio
tv device printer
ipod computer
battery sony
button headphones
nothing waste never
didn’t cannot problem
disappointed doesn’t
good great
-
product water coffee
steel tank kitchen
knives hot heat
maker design machine
work vacuum filter
don’t doesn’t
didn’t never problem
few broke less
disappointed poor
cheap nothing no
good great better nice
-
amazon product
service customer
arthur harold
bravo vincent
moor america
adventure comedy
Electronics-‐negaRve
31
コントロールされたトピックの単語分布
32. Book−negative
Book−positive
DVD−negative
DVD−poisitive
Electronics−negative
Electronics−positive
Kitchen−negative
Kitchen−positive
weight that did not correspond to labels
bias
−16
−14
−12
−10
−8
−6
−4
−2
0
2
4 Domain-‐Dependent
Domain-‐Independent
weight
parameters
Domain-‐Independent-‐neutral
Domain-‐Independent-‐posiRve
wine tea toaster
wonderful sturdy
sharp love great
good well easy
best better
didn’t never problem
few broke less
disappointed poor
cheap nothing no
good great better nice
good great love
work funny sexy
greatly best fans
amusing cool
thrilling succinctly
accurately
satisfying gracious
amazon product
service customer
quality warranty
support manufacturer
vendor
damage matter poor
scratched blame wrong
problem defective
arthur harold
bravo vincent
moor america
adventure comedy
minelli john
manhattan roxanne
bob napoleon
benjamin ghostbusters
book dvd
amazon
dependent topics, and we
se topics are positive or
ous values. Fourth, we
s the baseline model that
ndence in the sentiment
ical ratings from reviews
The experimental results showed two interesting findings.
First, DDITSM converged more rapidly than the baseline
model because of the strong constraint due to observed
domain information. Second, domain-independent topics
had positive, negative, and neutral polarities in the form
of continuous values. Neutral domain-independent topics
included proper nouns, and this means that proper nouns
-
-
arthur harold
bravo vincent
moor america
adventure comedy
minelli john
manhattan roxanne
bob napoleon
benjamin ghostbusters
book dvd
amazon
32
コントロールされたトピックの単語分布
33. Book−negative
Book−positive
DVD−negative
DVD−poisitive
Electronics−negative
Electronics−positive
Kitchen−negative
Kitchen−positive
weight that did not correspond to labels
bias
−16
−14
−12
−10
−8
−6
−4
−2
0
2
4 Domain-‐Dependent
Domain-‐Independent
weight
parameters
Domain-‐Independent-‐negaRve
knives hot heat
maker design machine
work vacuum filter
don’t doesn’t
didn’t never problem
few broke less
disappointed poor
cheap nothing no
good great better nice
-
amazon product
service customer
quality warranty
support manufacturer
vendor
damage matter poor
scratched blame wrong
problem defective
arthur harold
bravo vincent
moor america
adventure comedy
minelli john
manhattan roxanne
bob napoleon
benjamin ghostbusters
book dvd
amazon
The experimental results showed two interesting findings.
First, DDITSM converged more rapidly than the baseline
model because of the strong constraint due to observed
domain information. Second, domain-independent topics
had positive, negative, and neutral polarities in the form
of continuous values. Neutral domain-independent topics
included proper nouns, and this means that proper nouns
33
Complaints
about
the
e-‐commerce
site,
customer
support,
and
delivery
コントロールされたトピックの単語分布
36. References
36
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D.
M.
Blei
and
J.
D.
McAuliffe.
Supervised
topic
models.
In
Neural
InformaRon
Processing
Systems,
20:121–128,
2008.
[2]
D.
M.
Blei,
A.
Y.
Ng,
and
M.
I.
Jordan.
Latent
dirichlet
allocaRon.
Journal
of
Machine
Learning
Research,
3:993–1022,
2003.
[3]
D.
Ramage,
D.
Hall,
R.
NallapaR,
and
C.
D.
Manning.
Labeled
lda:
A
supervised
topic
model
for
credit
aNribuRon
in
mulR-‐labeled
corpora.
In
Proceedings
of
the
2009
Conference
on
Empirical
Methods
in
Natural
Language
Processing,
1:248–256,
2009.
[4]
Y.
W.
Teh,
M.
I.
Jordan,
M.
J.
Beal,
and
D.
M.
Blei.
Hierarchical
dirichlet
processes.
Journal
of
the
American
StaRsRcal
AssociaRon,
101(476):1566–1581,
2006.
[5]
J.
Blitzer,
M.
Dredze,
and
F.
Pereir.
Biographies,
bollywood,
boom-‐
boxes
and
blenders:
Domain
adaptaRon
for
senRment
classificaRon.
In
Annual
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For
ComputaRonal
LinguisRcs,
45(1):440,
2007.