Sentiment Analysis Technologies of Advertising Images Based on Deep Learning

With the rapid advancement of the digital economy, advertising images have emerged as a core medium for brand communication.Sentiment classification of such images plays a critical role in precision marketing and the enhancement of user experience.Recent progress in deep learning offers novel approaches to sentiment analysis in advertising images.However, existing methods remain constrained by limitations in multimodal information fusion, multi-granularity visual feature processing, and attribute-level sentiment interpretation.Common issues include the simplistic concatenation of visual and textual features, neglect of the emotional correlation between local and global visual elements, and the absence of effective integration of multi-scale attribute-level perspectives, all of which result in inadequate classification accuracy and robustness.To address these challenges, an attribute-level sentiment analysis model based on multi-level vision-language alignment and fusion for advertising images was proposed.Through a multi-granularity visual information alignment technique, the model enables precise semantic matching between visual elements-at the pixel, region, and object levels-and their corresponding textual counterparts.Furthermore, multi-scale attribute-level viewpoints were integrated to capture emotional features across dimensions such as color, shape, and embedded textual content.A text-centered multimodal training strategy was also designed to filter irrelevant visual noise.Experimental results demonstrate significant improvements in both accuracy and robustness of advertising image sentiment classification.This model provides technical support for advertising effectiveness evaluation and strategy optimization.The findings contribute theoretically to the advancement of multimodal sentiment analysis and offer practical guidance for precision advertising in contexts such as e-commerce and social media.