EAGNet: Enhanced aspect-guided heterogeneous graph attention network for multimodal aspect-based sentiment analysis

Key Points

• Sentiment classification accuracy increased significantly, achieving over 90% on multimodal datasets.
• Key evidence includes a comparative analysis showing a 15% improvement against baseline models.
• Analysis of multimodal aspects uses a newly developed heterogeneous graph attention network approach.
• These findings support enhanced techniques for understanding complex user sentiments across various data types.