Sentiment Analysis of Web Images by Integrating Machine Learning and Associative Reasoning Ideas
Yuan Fang, Yi Wang · International Journal of Advanced Computer Science and Applications · 2024
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.