Feature Specific Sentiment Analysis for Product Reviews, Subhabrata Mukherjee and Pushpak Bhattacharyya, In Proceedings of the 13th International Conference on Intelligent Text Processing and Computational Intelligence (CICLING 2012), New Delhi, India, March, 2012 (http://www.cse.iitb.ac.in/~pb/papers/cicling12-feature-specific-sa.pdf)
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Feature specific analysis of reviews
1. Feature Specific Sentiment
Analysis of Reviews
Subhabrata Mukherjee and Pushpak Bhattacharyya
Dept. of Computer Science and Engineering,
IIT Bombay
13th International Conference on Intelligent Text Processing
and Computational Intelligence - CICLING 2012,
New Delhi, India, March, 2012
2. MOTIVATION CONTDâŚ
ď¨ Sentiment Analysis is always with respect to a
particular entity or feature
ď¨ Feature may be implicit or explicit
ď¨ This work concerns explicit feature
3. MOTIVATION
ď¨I have an ipod and it is a great buy
but I'm probably the only person that
dislikes the iTunes software.
ď¤Here the sentiment w.r.t ipod is
positive whereas that respect to
software is negative
4. ENTITY AND FEATURES
ď¨ An entity may be analyzed from the point of
view of multiple features
ď¨ Entity â Titanic
ď¨ Features â Music, Direction, Plot etc.
ď¨ Given a sentence how to identify the set of
features ?
5. SCENARIO
ď¨ Each sentence can contain multiple features
and mixed opinions (positive and negative)
ď¨ Reviews mixed from various domains
ď¨ No prior information about set of features
except the target feature
6. MAIN FEATURES OF THE
ALGORITHM
ď¨ Does not require any prior information about
any domain
ď¨ Unsupervised â But need a small untagged
dataset to tune parameters
ď¨ Does not require any prior feature set
ď¨ Groups set of features into separate clusters
which need to be pruned or labeled
8. Hypothesis Example
ď¨ âI want to use Samsung which is a great
product but am not so sure about using
Nokiaâ.
ď¤ Here âgreatâ and âproductâ are related by an adjective modifier
relation, âproductâ and âSamsungâ are related by a relative
clause modifier relation. Thus âgreatâ and âSamsungâ are
transitively related.
ď¤ Here âgreatâ and âproductâ are more related to Samsung
than they are to Nokia
ď¤ Hence âgreatâ and âproductâ come together to express an
opinion about the entity âSamsungâ than about the entity
âNokiaâ
9. Hypothesis Example
ď¨ âI want to use Samsung which is a great
product but am not so sure about using
Nokiaâ.
ď¤ Here âgreatâ and âproductâ are related by an adjective modifier
relation, âproductâ and âSamsungâ are related by a relative
clause modifier relation. Thus âgreatâ and âSamsungâ are
transitively related.
ď¤ Here âgreatâ and âproductâ are more related to Samsung
than they are to Nokia
ď¤ Hence âgreatâ and âproductâ come together to express an
opinion about the entity âSamsungâ than about the entity
âNokiaâ
10. Hypothesis Example
ď¨ âI want to use Samsung which is a great
product but am not so sure about using
Nokiaâ.
ď¤ Here âgreatâ and âproductâ are related by an adjective modifier
relation, âproductâ and âSamsungâ are related by a relative
clause modifier relation. Thus âgreatâ and âSamsungâ are
transitively related.
ď¤ Here âgreatâ and âproductâ are more related to Samsung
than they are to Nokia
ď¤ Hence âgreatâ and âproductâ come together to express an
opinion about the entity âSamsungâ than about the entity
âNokiaâ
11. Hypothesis Example
ď¨ âI want to use Samsung which is a great
product but am not so sure about using
Nokiaâ.
ď¤ Here âgreatâ and âproductâ are related by an adjective modifier
relation, âproductâ and âSamsungâ are related by a relative
clause modifier relation. Thus âgreatâ and âSamsungâ are
transitively related.
ď¤ Here âgreatâ and âproductâ are more related to Samsung
than they are to Nokia
ď¤ Hence âgreatâ and âproductâ come together to express an
opinion about the entity âSamsungâ than about the entity
âNokiaâ
12. Hypothesis Example
ď¨ âI want to use Samsung which is a great
product but am not so sure about using
Nokiaâ.
ď¤ Here âgreatâ and âproductâ are related by an adjective modifier
relation, âproductâ and âSamsungâ are related by a relative
clause modifier relation. Thus âgreatâ and âSamsungâ are
transitively related.
ď¤ Here âgreatâ and âproductâ are more related to Samsung
than they are to Nokia
ď¤ Hence âgreatâ and âproductâ come together to express an
opinion about the entity âSamsungâ than about the entity
âNokiaâ
Adjective Modifier
13. Hypothesis Example
ď¨ âI want to use Samsung which is a great
product but am not so sure about using
Nokiaâ.
ď¤ Here âgreatâ and âproductâ are related by an adjective modifier
relation, âproductâ and âSamsungâ are related by a relative
clause modifier relation. Thus âgreatâ and âSamsungâ are
transitively related.
ď¤ Here âgreatâ and âproductâ are more related to Samsung
than they are to Nokia
ď¤ Hence âgreatâ and âproductâ come together to express an
opinion about the entity âSamsungâ than about the entity
âNokiaâ
Adjective Modifier
14. Hypothesis Example
ď¨ âI want to use Samsung which is a great
product but am not so sure about using
Nokiaâ.
ď¤ Here âgreatâ and âproductâ are related by an adjective modifier
relation, âproductâ and âSamsungâ are related by a relative
clause modifier relation. Thus âgreatâ and âSamsungâ are
transitively related.
ď¤ Here âgreatâ and âproductâ are more related to Samsung
than they are to Nokia
ď¤ Hence âgreatâ and âproductâ come together to express an
opinion about the entity âSamsungâ than about the entity
âNokiaâ
Adjective Modifier
15. Hypothesis Example
ď¨ âI want to use Samsung which is a great
product but am not so sure about using
Nokiaâ.
ď¤ Here âgreatâ and âproductâ are related by an adjective modifier
relation, âproductâ and âSamsungâ are related by a relative
clause modifier relation. Thus âgreatâ and âSamsungâ are
transitively related.
ď¤ Here âgreatâ and âproductâ are more related to Samsung
than they are to Nokia
ď¤ Hence âgreatâ and âproductâ come together to express an
opinion about the entity âSamsungâ than about the entity
âNokiaâ
Adjective Modifier
Relative Clause
Modifier
16. Example of a Review
ď¨ I have an ipod and it is a great buy but I'm
probably the only person that dislikes the
iTunes software.
17. Example of a Review
ď¨ I have an ipod and it is a great buy but I'm
probably the only person that dislikes the
iTunes software.
18. Example of a Review
ď¨ I have an ipod and it is a great buy but I'm
probably the only person that dislikes the
iTunes software.
19. Example of a Review
ď¨ I have an ipod and it is a great buy but I'm
probably the only person that dislikes the
iTunes software.
20. Example of a Review
ď¨ I have an ipod and it is a great buy but I'm
probably the only person that dislikes the
iTunes software.
22. Feature Extraction : Domain Info
Not Available
ď¨ Initially, all the Nouns are treated as features and added to
the feature list F.
ď
ď
ď
ď
ď
ď
ď
23. Feature Extraction : Domain Info
Not Available
ď¨ Initially, all the Nouns are treated as features and added to
the feature list F.
ď¨ F = { ipod, buy, person, software }
ď
ď
ď
ď
ď
ď
24. Feature Extraction : Domain Info
Not Available
ď¨ Initially, all the Nouns are treated as features and added to
the feature list F.
ď¨ F = { ipod, buy, person, software }
ď¨ Pruning the feature set
ď¤ Merge 2 features if they are strongly related
ď
ď
ď
ď
25. Feature Extraction : Domain Info
Not Available
ď¨ Initially, all the Nouns are treated as features and added to
the feature list F.
ď¨ F = { ipod, buy, person, software }
ď¨ Pruning the feature set
ď¤ Merge 2 features if they are strongly related
ď¨ âbuyâ merged with âipodâ, when target feature = âipodâ,
ď¤ âperson, softwareâ will be ignored.
ď
ď
26. Feature Extraction : Domain Info
Not Available
ď¨ Initially, all the Nouns are treated as features and added to
the feature list F.
ď¨ F = { ipod, buy, person, software }
ď¨ Pruning the feature set
ď¤ Merge 2 features if they are strongly related
ď¨ âbuyâ merged with âipodâ, when target feature = âipodâ,
ď¤ âperson, softwareâ will be ignored.
ď¨ âpersonâ merged with âsoftwareâ, when target feature =
âsoftwareâ
ď¤ âipod, buyâ will be ignored.
27. Relations
ď¨ Direct Neighbor Relation
ď¤ Capture short range dependencies
ď¤ Any 2 consecutive words (such that none of them is a
StopWord) are directly related
ď¤ Consider a sentence S and 2 consecutive words .
ď¤ If , then they are directly related.
ď¨ Dependency Relation
ď¤ Capture long range dependencies
ď¨ Let Dependency_Relation be the list of significant
relations.
ď¨ Any 2 words wi and wj in S are directly related, if
s.t.
42. Evaluation â Dataset 1
ď¨ 2500 sentences
ď¨ Varied domains like antivirus, camera, dvd, ipod,
music player, router, mobile
ď¨ Each sentence tagged with a feature and polarity
w.r.t the feature
ď¨ Acid Test
ď¤ Each Review has a mix of positive and negative
comments
43. Parameter Learning
ď¨ Dependency Parsing uses approx. 40 relations.
ď¨ Relation Space â (240 -1)
ď¨ Infeasible to probe the entire relation space.
ď¨ Fix relations certain to be significant
ď¤ nsubj, nsubjpass, dobj, amod, advmod, nn, neg
ď¨ Reject relations certain to be non-significant
44. Parameter Learning ContdâŚ
ď¨ This leaves around 21 relations some of which
may not be signficant.
ď¨ Compute Leave-One-Relation out accuracy
over a training set.
ď¨ Find the relations for which there is significant
accuracy change.
48. Significant Relations ContdâŚ
ď¨ Leaving out dep improves accuracy most
Relation Set Accuracy
With Dep+Rcmod 66
Without Dep 69
Without Rcmod 67
Without
Dep+Rcmod
68
49. Significant Relations ContdâŚ
ď¨ Leaving out dep improves accuracy most
Relation Set Accuracy
With Dep+Rcmod 66
Without Dep 69
Without Rcmod 67
Without
Dep+Rcmod
68
56. Evaluation â Dataset 2
ď¨ Extracted 500 sentences
ď¨ Varied domains like camera, laptop, mobile
ď¨ Each sentence tagged with a feature and
polarity w.r.t the feauture
âExploiting Coherence for the Simultaneous Discovery of Latent Facets and associated Sentimentsâ
60. CONCLUSIONS
ď¨ Incorporating feature specificity improves
sentiment accuracy.
ď¨ Dependency Relations capture long range
dependencies as is evident from accuracy
improvement.
ď¨ Work to be extended for implicit features and
domain dependent sentiment.