September 10, 2025Journal of Information Systems Engineering & ManagementOpen Access

Transforming Multimodal Sentiment Analysis and Classification with Fusion-Centric Deep Learning Techniques

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Authors

VVV VinithaDr. Hari Singh Gour University

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Implication

This study reveals the Enhanced Multi-modal Spatiotemporal Attention Network's potential in sentiment analysis, improving accuracy by addressing irrelevant data.

Key Points

The Enhanced Multi-modal Spatiotemporal Attention Network achieves 92.28% accuracy in sentiment and emotion prediction.
Extensive experiments on the Multimodal Emotion Lines Dataset confirm the robustness of the proposed approach.
Current methodologies often overlook the importance of text in multimodal sentiment analysis, addressing this gap.
The study highlights the effectiveness of deep learning techniques to integrate and enhance multimodal feature extraction.

Cite This Study

V Vinitha (2024) studied this question.

synapsesocial.com/papers/68c2443bb210217d647aa560 https://doi.org/10.52783/jisem.v9i4s.11886

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Implication

This study reveals the Enhanced Multi-modal Spatiotemporal Attention Network's potential in sentiment analysis, improving accuracy by addressing irrelevant data.

Transforming Multimodal Sentiment Analysis and Classification with Fusion-Centric Deep Learning Techniques

Discussion

Member takes

Implication

Key Points

Cite This Study

Transforming Multimodal Sentiment Analysis and Classification with Fusion-Centric Deep Learning Techniques

Discussion

Member takes

Implication

Key Points

Cite This Study

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