Adversarial Multimodal Sentiment Analysis with Cross-Modal and Cross-Domain Alignment

Abstract

Multimodal sentiment analysis faces significant challenges due to data scarcity and domain shift, which hinder model generalization across different datasets. To address these issues, we propose a Multimodal Domain Adaptation framework that combines advanced feature extraction techniques, attention-based fusion, and adversarial domain adaptation to enhance sentiment classification. Our approach builds on Domain-Adversarial Neural Networks (DANN) to facilitate knowledge transfer from a labeled source dataset to an unlabeled target dataset, reducing domain discrepancies while preserving sentiment prediction accuracy. Unlike existing methods that focus primarily on single-modality adaptation or basic feature alignment, our framework performs comprehensive cross-modal and cross-domain feature alignment to improve generalization. Specifically, we extract high-quality feature embeddings for both text and image modalities using state-of-the-art deep learning models. To bridge modal gaps, we integrate an attention-based fusion mechanism that prioritizes the most informative modality, ensuring optimal feature integration. Additionally, we employ a Gradient Reversal Layer (GRL) and a domain discriminator to reduce domain loss, enabling the model to learn domain-invariant representations. Our experimental results demonstrate that Adversarial Multimodal Sentiment Adaptation with Cross-Modal and Cross-Domain Alignment (AMSA-CMCDA) significantly enhance performance in sentiment classification across datasets. By effectively addressing both modality mismatch and domain shift, our approach proves its effectiveness in real-world sentiment analysis applications.