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Co-Training for Cross-Lingual Sentiment Classification

Xiaojun Wan (萬小軍 )

Associate Professor, Peking University

ACL 2009

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Research Gap

• Opinion mining has drawn much attention recently– Sentiment classification (POS, NEG, NEU)– Subjectivity analysis (subjective, objective)

• Annotated corpora are most important for training

• However, most of them are English data

• Corpora for other languages, including Chinese, are rare

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Related Work

• Pilot studies on cross-lingual subjectivity classification

• Mihalcea et al. ACL 2007– Bilingual lexicon and manually translated parallel

corpus• Banea et al. EMNLP 2008

– English annotation tool + MT– Build Romanian annotation tool– Not much loss compared to human translation– Suggesting MT is a viable way

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Problem Definition

• Perform cross-lingual sentiment classification– Either positive or negative

• Source: English

• Target: Chinese

• Leverage– 8000 Labeled English product reviews

– 1000 Unlabeled Chinese product reviews

– Machine translation (MT)

• Derive– Sentiment classification tools for Chinese product reviews

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Framework

• Training Phase

• Classification Phase

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Training Phase (1)Machine Translation

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Two Views

Chinese View English View

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Training Phase (2)The Co-Training Approach

English View

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Label the unlabeled data (English)

English Classifierwith SVM

Label

Een

Top p positiveTop n negative

most confidentreview

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Label the unlabeled data (Chinese)

Chinese Classifierwith SVM

Ecn

Top p positiveTop n negativemost confident

review

Label

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Remove from Unlabeled DataFinish one Iteration

Een

Top p positiveTop n negative

most confidentreview

Ecn

Top p positiveTop n negativemost confident

review

∪

Train again Train again

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Setting

• #Iteration = 40

• p = n = 5

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Classification Phase

Chinese Classifier

English Classifier

average [-1, 1]

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Experiment Setting (Training)

8000 Amazonproduct reviews.

4000 positive4000 negative

Books, DVDs,electronics

1000 product reviews fromwww.it168.com

mp3 player,mobile phones,DC

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Experiment Setting (Testing)

• 886 Chinese product reviews from www.it168.com– 451 positive, 435 negative

– Different from unlabeled training data (outside testing)

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Baseline

• SVM– Use only labeled data

• TSVM (Transductive SVM)– Joachims, 1999– Use both labeled and unlabeled

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SVM Baselines

SVM(EN)SVM(CN)

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SVM Baselines

SVM(ENCN1)

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SVM Baselines

SVM(ENCN2)

average

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TSVM Baselines

TSVM(EN)TSVM(CN)

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TSVM Baselines

TSVM(ENCN1)

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TSVM BaselinesTSVM(ENCN2)

average

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Result: Method Comparison (1)

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Result: Method Comparison (2)Performance on Each Side

SVM(EN)

TSVM(EN)

CoTrain(EN)

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Result: Method Comparison (3)

Accuracy

SVM(EN) 0.738

TSVM(EN) 0.769

CoTrain(EN) 0.790

Accuracy

SVM(CN) 0.771

TSVM(CN) 0.767

CoTrain(CN) 0.775

CoTrain make better use of unlabeled Chinese reviews than TSVM

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Result: Iteration Number Outperform TSVM(ENCN2) after 20 iterations

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Result: Balance of (p,n) Unbalanced examples hurt the performance badly

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Conclusion & Comment

• Co-Training approach for cross-lingual sentiment classification

• Future Work– Translated and natural text have different feature

distribution

– Domain adaptation algorithm (ex. structural correspondence learning) for linking them

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Comment

• Leverage word (phrase) alignment in translated text