Abstract: To achieve automatic recognition and understanding of image sentiment analysis, the study proposes an image sentiment prediction network based on multi-excitation fusion. This network simultaneously handles multiple excitations, such as color, object, and face, and is designed to predict the sentiment associated with an image. A visual emotion inference network based on scene-object association is proposed using the association reasoning method to describe the emotional associations between different objects. The multi-excitation fusion image sentiment prediction network achieved the highest accuracy of 75.6% when the loss weight was 1.0. The network had the highest accuracy of 76.5% when the object frame data was 10. The average accuracy of the visual sentiment inference network based on scene-object association was 91.8%, which was an improvement of about 3.7% compared to the image sentiment association analysis model. The outcomes revealed that the multi-stimulus fusion method performed better in the image emotion prediction task. The visual emotion inference network based on scene-object association can recognize objects and scenes in images more accurately, and both the scene-based attention mechanism and the masking operation can improve the network performance. This research provides a more effective approach to the field of image sentiment analysis and helps to improve the computer's ability to recognize and understand emotional expressions.

Yuan Fang and Yi Wang, “Sentiment Analysis of Web Images by Integrating Machine Learning and Associative Reasoning Ideas” International Journal of Advanced Computer Science and Applications(IJACSA), 15(12), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151273

@article{Fang2024,
title = {Sentiment Analysis of Web Images by Integrating Machine Learning and Associative Reasoning Ideas},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151273},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151273},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {12},
author = {Yuan Fang and Yi Wang}
}