Noise-aware Graph Neural Networks for multimodal semantic alignment in social media sentiment analysis

Key Points

• Improved sentiment analysis accuracy using noise-aware graph neural networks was observed in social media data.
• A notable increase of 15% in performance metrics indicates the effectiveness of multimodal approaches in aligning semantic meaning.
• Analysis of social media posts utilized advanced noise-aware techniques to enhance the robustness of sentiment interpretations.
• Highlights the need for novel methodologies in sentiment analysis, particularly in noisy data environments.