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Attention-Based Fusion of Intra- and Intermodal Dynamics in Multimodal Sentiment Analysis | IEEE Conference Publication | IEEE Xplore

Attention-Based Fusion of Intra- and Intermodal Dynamics in Multimodal Sentiment Analysis

Publisher: IEEE

Abstract:

Sentiment analysis, the process of predicting sentiments expressed in human communication, has evolved to multimodal sentiment analysis (MSA). Recent advances in attentio...View more

Abstract:

Sentiment analysis, the process of predicting sentiments expressed in human communication, has evolved to multimodal sentiment analysis (MSA). Recent advances in attention-based MSA models have demonstrated the effectiveness of capturing intramodality and intermodality dynamics. However, challenges remain in achieving optimal performance and creating effective context representations across modalities. To address these challenges, we propose the Multi-Head self-attention with Context-Aware attention model, which utilizes two attention-based mechanisms to strategically capture intramodality dynamics within each modality before delving into intermodality dynamics. The experimental results on 5 datasets show the superiority of our model in comparison with the state of the arts.
Date of Conference: 11-15 March 2024
Date Added to IEEE Xplore: 23 April 2024
ISBN Information:

ISSN Information:

Publisher: IEEE
Conference Location: Biarritz, France

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