Information Technology Reference
In-Depth Information
In the following, we describe how opine finds the semantic orientation of
words.
Context-Specific Word Semantic Orientation
Given a set of
semantic orientation (SO) labels
(
),
a set of reviews and a set of tuples (
w
,
f
,
s
), where
w
is a potential opinion
word associated with feature
f
in sentence
s
, opine assigns a SO label to each
tuple (
w
,
f
,
s
). For example, the tuple (
sluggish
,
driver
, “I am not happy with
this sluggish driver”) will be assigned a
negative
SO label
2
.
opine uses the three-step approach below to label each (
w
,
f
,
s
) tuple:
1. Given the set of reviews, opine finds a SO label for each word
w
.
2. Given the set of reviews and the set of SO labels for words
w
, opine
finds a SO label for each (
w
,
f
)pair.
3. Given the set of SO labels for (
w
,
f
) pairs, opine finds a SO label for
each (
w
,
f
,
s
) input tuple.
Each of these subtasks is cast as an unsupervised collective classification
problem and solved using the same mechanism. In each case, opine is given
a set of
objects
(words, pairs or tuples) and a set of
labels
(SO labels); opine
then searches for a
global
assignment of labels to objects. In each case, opine
makes use of
local constraints
on label assignments (
e.g.
, conjunctions and
disjunctions constraining the assignment of SO labels to words [10]).
A key insight in opine is that the problem of searching for a
global
SO
label assignment to words, pairs, or tuples while trying to satisfy as many
local
constraints on assignments as possible is analogous to labeling problems
in computer vision (
e.g.
, model-based matching). opine uses a well-known
computer vision technique,
relaxation labeling
[11], in order to solve the three
subtasks described above.
{
positive, negative, neutral
}
Relaxation Labeling Overview
Relaxation labeling is an unsupervised classification technique which takes as
input:
a) a set of
objects
(
e.g.
, words)
b) a set of
labels
(
e.g.
,SOlabels)
c) initial probabilities for each object's possible labels
d) the definition of an object
o
's
neighborhood
(a set of other objects which
influence the choice of
o
's label)
e) the definition of
neighborhood features
f) the definition of a
support function
for an object label
The influence of an object
o
's neighborhood on its label
L
is quantified
using the
support function
. The support function computes the probability
of the label
L
being assigned to
o
as a function of
o
's
neighborhood features
.
2
We use “word” to refer to a potential opinion word
w
and “feature” to refer to
the word or phrase which represents the explicit feature
f
.
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