Extracting Product Features and Opinions from Reviews - Natural Language Processing and Text Mining - page 17

Examples of features include the fact that a certain local constraint is satisfied
( e.g. ,theword nice participates in the conjunction and together with some
other word whose SO label is estimated to be positive ).
Relaxation labeling is an iterative procedure whose output is an assign-
ment of labels to objects. At each iteration, the algorithm uses an update
equation to reestimate the probability of an object label based on its previ-
ous probability estimate and the features of its neighborhood. The algorithm
stops when the global label assignment stays constant over multiple consecu-
tive iterations.
We employ relaxation labeling for the following reasons: a) it has been
extensively used in computer-vision with good results and b) its formalism
allows for many types of constraints on label assignments to be used simul-
taneously. As mentioned before, constraints are integrated into the algorithm
as neighborhood features which influence the assignment of a particular label
to a particular object.
opine uses the following sources of constraints:
a) conjunctions and disjunctions in the review text
b) manually supplied syntactic dependency rule templates (see Table 2.8).
The templates are automatically instantiated by our system with different
dependency relationships (premodifier, postmodifier, etc.) in order to obtain
syntactic dependency rules which find words with related SO labels.
c) automatically derived morphological relationships ( e.g. , “wonderful” and
“wonderfully” are likely to have similar SO labels).
d) WordNet-supplied synonymy, antonymy, IS-A and morphological rela-
tionships between words. For example, clean and neat are synonyms and so
they are likely to have similar SO labels.
Each of the SO label assignment subtasks previously identified is solved
using a relaxation labeling step. In the following, we describe in detail how
relaxation labeling is used to find SO labels for words in the given review sets.
Finding SO Labels for Words
For many words, a word sense or set of senses is used throughout the re-
view corpus with a consistently positive, negative or neutral connotation ( e.g. ,
“great,” “awful,” etc.). Thus, in many cases, a word w 's SO label in the con-
text of a feature f and sentence s will be the same as its SO label in the
context of other features and sentences. In the following, we describe how
opine's relaxation labeling mechanism is used to find a word's dominant SO
label in a set of reviews.
For this task, a word's neighborhood is defined as the set of words connected
to it through conjunctions, disjunctions, and all other relationships previously
introduced as sources of constraints.
RL uses an update equation to re-estimate the probability of a word label
based on its previous probability estimate and the features of its neighbor-
hood (see Neighborhood Features ). At iteration m ,let q ( w, L ) ( m ) denote