This is a presentation we made in the 2012 Spring Data Mining class of Tsinghua University. The presentation is about a paper by Lei Zhang, Bing Liu, Suk Hwan Lim, Eamonn O’Brien-Strain

1. Extracting and Ranking Product
Features in Opinion Documents
陈欣，王鹤达，张文昌

2. • Retina Display
• 3-axis gyro & accelerometer
• A4 CPU
• Multitask
• Face Time
• iBook
• Antenna Gate
A Story

3. • Clearly knowing the response from
consumers will help company win
more market share.
• Consumers could also make correct
choices when shopping.
Why mining product
features?

4. • In recent years, opinion mining has been an active research
area in NLP. The most important problem is to extracting
features from a corpus.
• HMM, ME, PMI,CRF methods.
• Double Propagation is a state-of-art unsupervised technique
for solving this problem, though it has its own significant
limitations.
Recent Research

5. • Proposed by researchers from
Illinois University and Zhejiang
University.
• Mainly extracts noun
features, woks well for medium-
size corpora.
• No additional resources but initial
seed opinion lexicon needed.
Double Propagation

6.  Basic Assumption: Features are nouns/noun phrases and opinion words are
adjectives.
 Dependency Grammar: Describe the dependency relations between words in a
sentence, including direct relations(a)(b) and indirect relations(c)(d).
Opinion
The camera has a good lens.
Class Feature
DP Mechanism

7.  Non-opinion adjectives may be extracted as opinion words. This will introduce more
and more noise during the extracting process.
current
+ Noun
entire
 Some important features do not have opinion words modifying them.
There is a valley on my mattress.
No opinion word modified feature
DP Limitations

8. Two-Step feature mining method:
 Feature Extraction
• Double Propagation
• Part-whole pattern
• No pattern
 Feature Ranking
• New angle to solve the noise problem.
• Use relevance & frequency to rank features.
Proposed Methods

9. • Three strong clue indicates a correct feature:
• Modified by multiple opinion words.
• Could be extracted by multiple part-whole pattern.
• Combination of the part-whole, no pattern and opinion word
modification.
• Frequent appearing indicates an important feature.
Ranking Principles

10. • Feature extraction
• part-whole relation
• “no” pattern
• Feature ranking
• HITS algorithm
• consider frequency
Process

11. • Ambiguous / Unambiguous
• Phrase pattern
• NP + Prep + CP
• CP + with + NP
• NP CP or CP NP
• Sentence pattern
• CP Verb NP
Part-whole relation

12. • no + features
• no noise, no indentation
• Exceptions
• no problem, no offense
• manually compiled an exception list
“no” Pattern

13. • HITS Algorithm
• hub score / authority score
• iteration to optimize
• Apply HITS Algorithm
• split feature and feature indicator
• use directed graph
• compute feature revelance
Apply HITS Algorithm

14. •
Feature Ranking

15. Data Sets Cars Mattress Phone LCD
# of Sent. 2223 13233 15168 1783
“Cars” and “Mattress”: product review sites.
“Phone” and “LCD”: forum sites.
Precision@N metric:
Percentage of correct features that are among
the top N feature candidates in a ranked list.
Data Sets & Evaluation Metrics

16. 0.79 0.79
0.78 0.77
0.8 0.69 0.68
0.68
0.66
0.7 0.64
0.55 0.54
0.6 0.56
0.55
0.5 0.43 Our Recall
0.44
DP Recall
0.4
Our Precision
0.3 0.23 DP Precision
Our Precision DP Precision
0.2 DP Recall
Our Recall
Results of 1000 sentences
Recall & Precision Comparison

17. 0.7 0.7 0.7
0.7
0.67
0.69 0.66
0.7 0.65 0.64
0.66
0.62
0.65
0.58
0.6
0.56
0.55 0.52 Our Recall
0.5 0.5 DP Recall
0.42 Our Precision
0.45 DP Precision
Our Precision DP Precision
0.4 DP Recall
Our Recall
Results of 2000 sentences
Recall & Precision Comparison

18. 0.65
0.66
0.7 0.67
0.64
0.62
0.65
0.59
Our Recall
0.6
DP Recall
0.55 Our Precision
0.51
0.48 DP Precision DP Precision
0.5 Our Precision
DP Recall
0.45 Our Recall
Mattress
Phone
Results of 3000 sentences
Recall & Precision Comparison

19. 1 0.94
0.9 0.84 0.9
0.81
0.8 0.76 0.76
0.7 0.64 0.68 Our Precision
DP Precision
0.6 DP Precision
Our Precision
Precision at top 50
Ranking Comparison

20. 0.9 0.88
0.82
0.85
0.85 0.8
0.8
0.75 0.75
0.73
0.7 0.65 0.68 Our Precision
0.65 DP Precision
0.6 DP Precision
Our Precision
Precision at top 100
Ranking Comparison

21. 0.8
0.8 0.79
0.75
0.76
0.75 0.71
0.7 Our Precision
0.7 DP Precision
DP Precision
0.65
Our Precision
Cars
Mattress
Phone
Precision at top 200
Ranking Comparison

22. • Use part-whole and “no” patterns to increase recall
• Rank extracted feature candidates by feature
importance, determined by two factors:
• Feature relevance
• Feature frequency(HITS was applied)
Conclusion

23. Thank you