A multi-relational graph convolutional network for multimodal sentiment analysis

Key Points

• This research aims to enhance sentiment analysis by employing a multi-relational graph convolutional network.
• Utilized a graph convolutional network architecture to process and analyze multimodal data sources.
• Implemented sentiment analysis techniques across various data modalities like text, audio, and visual elements.
• Evaluated the model's performance using benchmark datasets for sentiment classification.
• Achieved improved accuracy rates for sentiment classification compared to traditional methods.
• Demonstrated robust performance across multiple data modalities, enhancing the understanding of sentiment in different contexts.

Abstract