Selecting Attributes for Sentiment Classification Using Feature Relation Networks

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


OutlinesMotivationObjectivesMethodologyExperimentsConclusionsComments


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


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”


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.


Methodology-Extended N-Gram Feature Set


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.


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.


Methodology - The Complete Network


Methodology - Incorporating Semantic　 Information


Experiments - Datasets


Experiments – FRN vs Univariate


Experiments - FRN vs Univariate (WithinOne)


Experiments - FRN vs Multivariate


Experiments - FRN vs Multivariate (WithinOne)


Experiments - FRN vs Hybrid


Experiments - FRN vs Hybrid (WithinOne)


Experiments - Ablation


Experiments - Parametert (0.0005, 0.005, 0.05, and 0.5)p (0.80, 0.90, and 1.00)


Experiments - Average Runtimes


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.


Comments• Advantages

- accuracy 、 computationally efficient• Disadvantage

- ablation and parameter is sensitive• Applications

- sentiment classification- feature selection method

Selecting Attributes for Sentiment Classification Using Feature Relation Networks

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