Context aware semantic adaptation network for cross domain implicit sentiment classification.

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Machine Learning

Background:

• Cross-domain sentiment classification faces challenges in reducing domain discrepancy while extracting effective text representations.
• Existing methods often prioritize domain-invariant features, neglecting valuable domain-specific semantic information crucial for implicit sentiment classification.
• Traditional adversarial models focus on global distribution alignment, potentially overlooking the maximization of class-specific decision boundaries.

Purpose of the Study:

• To propose a novel Context-Aware Semantic Adaptation (CASA) network for cross-domain implicit sentiment classification (ISC).
• To enhance the understanding of emotion-changing processes in ISC tasks, particularly those lacking explicit emotion words.
• To leverage both inter- and intrasentence semantic associations and domain-specific features for improved classification accuracy.

Main Methods:

• Development of a context-aware heterogeneous graph (CAHG) to capture intrasentence dependencies and intersentence interactions.
• Application of an attention mechanism to retain high-level domain-specific features within the CAHG.
• Introduction of a multigrain discriminator (MGD) to reduce interdomain distribution discrepancy and enhance intradomain class discrimination.

Main Results:

• The proposed CASA network demonstrates effectiveness in cross-domain implicit sentiment classification.
• Experimental results validate the contribution of individual modules (CAHG and MGD) compared to existing models.
• The model achieves strong performance on a Chinese implicit emotion dataset and four public explicit datasets.

Conclusions:

• The CASA network effectively addresses limitations in existing cross-domain sentiment classification methods by incorporating domain-specific semantics.
• The integration of CAHG and MGD significantly improves the model's ability to handle implicit sentiment and reduce domain discrepancies.
• The study highlights the potential of context-aware semantic adaptation for advancing implicit sentiment analysis across different domains.