Fusion-Driven Approaches for Multimodal Sentiment Analysis Using  Machine Learning and Deep Learning

Mayank Devani,Dr. Harsha Padheriya,Vijaysinh Jadeja,Mikin Dagli

Fusion-Driven Approaches for Multimodal Sentiment Analysis Using Machine Learning and Deep Learning

Authors

Mayank Devani,Dr. Harsha Padheriya,Vijaysinh Jadeja,Mikin Dagli

Keywords:

Multimodal Sentiment Analysis [3], Affective Computing, Fusion Techniques, Early Fusion, Late Fusion, Machine Learning, Deep Learning, Audio-Visual Sentiment Analysis, Benchmark Datasets, Opinion Mining

Abstract

Sentiment analysis has changed from traditional text-based methods to multimodal frameworksthat integrate text, audio, and visual modalities in response to the exponential growth of usergenerated material on social media.

References

Zadeh, A., Chen, M., Poria, S., Cambria, E., & Morency, L. P., “MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos,” arXiv preprint arXiv:1606.06259, 2016.

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Published

2023-04-20

How to Cite

Mayank Devani,Dr. Harsha Padheriya,Vijaysinh Jadeja,Mikin Dagli. (2023). Fusion-Driven Approaches for Multimodal Sentiment Analysis Using Machine Learning and Deep Learning. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 1555–1568. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/3403