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DOI: 10.14569/IJACSA.2025.01602118
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Enhancing Emotion Prediction in Multimedia Content Through Multi-Task Learning

Author 1: Wan Fan

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 16 Issue 2, 2025.

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Abstract: This study presents a robust multimodal emotion analysis model aimed at improving emotion prediction in film and television communication. Addressing challenges in modal fusion and data association, the model integrates a Transformer-based framework with multi-task learning to capture emotional associations and temporal features across various modalities. It overcomes the limitations of single-modal labels by incorporating multi-task learning, and is tested on the Cmumosi dataset using both classification and regression tasks. The model achieves strong performance, with an average absolute error of 0.70, a Pearson correlation coefficient of 0.82, and an accuracy of 47.1% in a seven-class task. In a two-class task, it achieves an accuracy and F1 score of 88.4%. Predictions for specific video segments are highly consistent with actual labels, with predicted scores of 2.15 and 1.4. This research offers a new approach to multimodal emotion analysis, providing valuable insights for film and television content creation and setting the foundation for further advancements in this area.

Keywords: Multi task learning; multimodal emotion analysis; timing; transformer; attention

Wan Fan, “Enhancing Emotion Prediction in Multimedia Content Through Multi-Task Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.01602118

@article{Fan2025,
title = {Enhancing Emotion Prediction in Multimedia Content Through Multi-Task Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01602118},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01602118},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Wan Fan}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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