Learning Disentangled Representation for Multimodal Cross-Domain Sentiment Analysis.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Machine Learning

Background:

• Multimodal cross-domain sentiment analysis faces challenges due to insufficient labeled data.
• Existing methods struggle with domain-specific expressive styles that hinder adaptation.
• Effective sentiment information transfer across domains is crucial for performance.

Purpose of the Study:

• To propose a novel method for multimodal cross-domain sentiment analysis.
• To reduce domain shift caused by expressive styles in multimodal data.
• To enhance the transferability of sentiment information across different datasets.

Main Methods:

• Developed a disentangled sentiment representation adversarial network (DiSRAN).
• Employed a cross-modality attention layer to align multiple modalities and obtain joint representations.
• Utilized adversarial training to disentangle domain-invariant sentiment representations from expressive styles.

Main Results:

• The proposed DiSRAN method effectively reduces domain shift in expressive styles.
• The disentangled sentiment representations are domain-invariant, facilitating better information transfer.
• Experimental results show superior performance compared to state-of-the-art approaches on benchmark tasks.

Conclusions:

• The DiSRAN method offers an effective solution for multimodal cross-domain sentiment analysis.
• Disentangling expressive styles is key to improving sentiment adaptation across domains.
• The approach shows significant potential for applications requiring cross-dataset sentiment understanding.