Selecting Attributes for Sentiment Classification Using Feature Relation Networks
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Transcript of Selecting Attributes for Sentiment Classification Using Feature Relation Networks
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Intelligent Database Systems Lab
Presenter : JIAN-REN CHEN
Authors : Ahmed Abbasi, Stephen France, Zhu Zhang,
and Hsinchun Chen
2011 , IEEE TKDE
Selecting Attributes for Sentiment Classification Using Feature Relation Networks
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Intelligent Database Systems Lab
OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments
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Intelligent Database Systems Lab
MotivationSentiment analysis has emerged as a method for
mining opinions from such text archives.
challenging problem:
1. requires the use of large quantities of linguistic features
2. integrate these heterogeneous n-gram categories into a single
feature set
- noise 、 redundancy and computational limitations
1) polarity 2) intensityI don’t like you 、 I hate you
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n-gram - (Markov model)天氣:晴天、陰天、雨天美麗 vs 美痢
“HAPAX” and “DIS” tagsI hate Jimreplaced with “I hate HAPAX”
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Objectives• Feature Relation Network (FRN) considers semantic information
and also leverages the syntactic relationships between n-gram
features.
- enhanced sentiment classification on extended sets of
heterogeneous n-gram features.
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Intelligent Database Systems Lab
Methodology-Extended N-Gram Feature Set
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Methodology - Subsumption Relations
A subsumes B(A → B) “I love chocolate”
unigram : I, LOVE, CHOCOLATE bigrams : I LOVE, LOVE CHOCOLATE trigrams : I LOVE CHOCOLATE
W hat about the bigrams and trigrams?It depends on their weight.Their weight exceeds that of their general lower order counterparts by threshold t.
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Methodology - Parallel RelationsA parallel B (A - B)
POS tag: “ADMIRE_VP” → “ like” semantic class: “SYN-Affection” → “ love”
A and B have a correlation coefficient greater than some threshold p, one of the attributes is removed to avoid redundancy.
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Methodology - The Complete Network
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Methodology - Incorporating Semantic Information
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Experiments - Datasets
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Experiments – FRN vs Univariate
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Experiments - FRN vs Univariate (WithinOne)
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Experiments - FRN vs Multivariate
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Experiments - FRN vs Multivariate (WithinOne)
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Experiments - FRN vs Hybrid
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Experiments - FRN vs Hybrid (WithinOne)
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Experiments - Ablation
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Experiments - Parametert (0.0005, 0.005, 0.05, and 0.5)p (0.80, 0.90, and 1.00)
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Experiments - Average Runtimes
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Conclusions• FRN had significantly higher best accuracy and best
percentage within-one across three testbeds.
• The ablation and parameter testing results play an
important role for the subsumption and parallel
relation thresholds.
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Comments• Advantages
- accuracy 、 computationally efficient• Disadvantage
- ablation and parameter is sensitive• Applications
- sentiment classification- feature selection method