Extracting and Ranking Product Features in Opinion Documents
Lei Zhang
#,
Bing Liu #, Suk Hwan Lim *, Eamonn O’Brien-Strain *
# University of Illinois at Chicago
* HP Labs
1. Introduction
3.2 Feature Ranking
An important task of opinion mining is to extract people’s opinions on features/attributes
of an entity. The sentence, “I love the GPS function of Motorola Droid”, expresses a
positive opinion on the “GPS function” of the Motorola phone. “GPS function” is the feature.
The basic idea is to rank the extracted feature candidates by feature importance. If a
feature candidate is correct and important, it should be ranked high. For unimportant
feature or noise, it should be ranked low.
2. Existing Techniques
We identify two major factors affecting the feature importance.
Feature relevance: it describes how possible a feature candidate is a correct feature.
A recently proposed unsupervised technique for extracting features from reviews
2.1 Double Propagation (Qiu et al 2010)
Observation :
 Opinion words are often used to modify features ; opinion words and features themselves
have relations in opinionated expressions too,
Method:
 Double propagation assumes that features are nouns/noun phrases and opinion words are
adjectives
 Opinion words can be recognized by identified features, and features can be identified by
known opinion words. The extracted opinion words and features are utilized to identify new
opinion words and new features which are used again to extract more opinion words and
features. This propagation or bootstrapping process ends when no more opinion words or
features can be found.
 The opinion word/feature relations can be identified via a dependency parser based on the
dependency grammar.
Feature frequency: a feature is important, if appears frequently in opinion documents.
We find that there is a mutual enforcement relation between opinion words, part-whole
relation and “no” patterns and features. If an adjective modifies many correct features,
it is highly possible to be a good opinion word. Similarly, if a feature candidate can be
extracted by many opinion words, part-whole patterns, or “no” pattern, it is also highly
likely to be a correct feature. The Web page ranking algorithm HITS is applicable.
Bipartite graph and HITS algorithm
2.2. Dependency Grammar
It describes the dependency relations between words in a sentence
Direct relations: it represents that one word depends on the other word directly or they
both depend on a third word directly (e.g. “ The camera has a good lens” )
Indirect relations: it represents that one word depends on the other word through other
words or they both depend on a third word indirectly
Dependency relations
3.3. The Whole Algorithm
Step 1 : Extract products features using double propagation, part-whole patterns and
“no” patterns
Step 2 : Compute feature score using HITS without considering frequency
Step 3 : The final score function considering the feature frequency is given as follows
S = S(f) * log( freq(f) )
Freq (f) is the frequency count of feature f, and S(f) is the authority score of the
feature f.
Feature extraction and ranking
Opinion
Lexicon
Parsing indirect relations is error-prone for Web corpora. Thus we only use direct relation to
extract opinion words and feature candidates in our application.
Rank features
Algorithms
Feature extraction
double propagation, part-whole
relation, “no” pattern”
Class
Concept
Word
Feature ranking
HITS algorithm
Video
Picture quality
GPS function
.
.
.
2.3. Weakness
For large corpora, double propagation may introduce a lotof noise ( error propagation).
For small corpora, it may miss some important features ( features are not modified by any
opinion word).
Corpus1
3. Proposed Techniques
To deal with the problem of double propagation, we propose a novel method to mine features,
which consists of two steps: feature extraction and feature ranking.
3.1 Feature Extraction
We still adopt double propagation idea to populate feature candidates. But two improvements
based on part-whole relation patterns and a “no” pattern are made to find features which double
propagation cannot find.
Corpus2
…
Corpusn
4. Experiments
We used 4 diverse corpora to evaluate the techniques. They were obtained from a
commercial company. The data were crawled and extracted from multiple online message
boards and blogs discussing different products and services.
Table 1. Descriptions of the 4 corpora
So there are three kinds of feature indicators:
 Double propagation
 Part-whole relation pattern
A part-whole pattern indicates one object is part of another object. It is a good indicator for
features if the class concept word (the “whole” part) is known.
Table 2. Results of 1000 sentences
(1) Phrase pattern
NP + Prep + CP
CP + with + NP
NP CP or CP NP
(e.g. battery of the camera)
(e.g. mattress with a cover)
(e.g. mattress pad )
Table 3. Precision at top 50 feature candidates
(2) Sentence pattern
CP verb NP
(e.g. the phone has a big screen)
 “no” pattern
4. Conclusions
a specific pattern for product review and forum posts. People often express their comments
or opinions on features by this short pattern (e.g. no noise)
A new method to deal with problems of double propagation. Part-whole and “no” patterns are used to
increase recall and then ranks the extracted feature candidates by feature importance, which is
determined by feature relevance and frequency. HITS was applied to compute feature relevance.