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LADA: A label-aware framework for cross-domain sentiment classification.

Yu Tong1, Ying Chen1, Xupeng Mai1

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Summary
This summary is machine-generated.

This study introduces a Label-Aware Domain Adaptation (LADA) framework to improve cross-domain sentiment analysis. LADA effectively aligns feature distributions while preserving label relationships, outperforming existing methods.

Keywords:
Cross domainMulti sourceSentiment analysis

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Cross-domain sentiment analysis faces challenges in aligning feature distributions without preserving feature-label relationships.
  • Existing methods like distance metric alignment and generative-adversarial networks have limitations in generating truly domain-invariant and relevant features.

Purpose of the Study:

  • To introduce a novel Label-Aware Domain Adaptation (LADA) framework for enhanced cross-domain sentiment analysis.
  • To address the limitations of current domain adaptation techniques by preserving the relationship between features and labels.

Main Methods:

  • LADA utilizes the joint probability distribution to maintain feature-label relationships.
  • It aligns the joint feature distributions of source and target domains to generate domain-invariant features.
  • The framework incorporates label information directly into the domain adaptation process.

Main Results:

  • Comprehensive experiments demonstrate the effectiveness of LADA in cross-domain sentiment analysis.
  • LADA achieves state-of-the-art performance on benchmark sentiment analysis tests.
  • The proposed method successfully generates domain-invariant features while preserving crucial label information.

Conclusions:

  • LADA offers a significant advancement in cross-domain sentiment analysis by effectively integrating label awareness.
  • The framework overcomes key limitations of previous domain adaptation approaches.
  • LADA demonstrates robust performance and establishes a new state-of-the-art in the field.