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This is a Quantitative Study study.

September 17, 2025Mitteilungen Klosterneuburg

Sentiment Analysis From Multimodal Data Using Joint Representations and Adaptive Transformer Optimization

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Authors

PVP. VasanthiVellore Institute of Technology University VVV. Madhu ViswanathamVellore Institute of Technology University

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Implication

Novel framework enhances sentiment analysis accuracy using joint representations from audio, video, and text data, suggesting better integration methods.

Key Points

The proposed framework achieved an accuracy of 95.11%, demonstrating significant improvements in sentiment classification.
Extensive validation on CMU-MOSI and CMU-MOSEI datasets showcases the framework's superior performance against existing approaches.
Using cyclic translation, the framework effectively maintains consistency between modalities while isolating meaningful patterns.
Adaptive dragon optimization enhances model precision by combining unimodal features with complementary bimodal information.

Cite This Study

Vasanthi et al. (2025) studied this question.

synapsesocial.com/papers/68d4567431b076d99fa5bf2b https://doi.org/10.61586/rdtmo

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Implication

Novel framework enhances sentiment analysis accuracy using joint representations from audio, video, and text data, suggesting better integration methods.

Sentiment Analysis From Multimodal Data Using Joint Representations and Adaptive Transformer Optimization

Discussion

Member takes

Implication

Key Points

Cite This Study

Sentiment Analysis From Multimodal Data Using Joint Representations and Adaptive Transformer Optimization

Discussion

Member takes

Implication

Key Points

Cite This Study

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