Intelligent Visual Narratives: Integrating Generative Adversarial Networks and NLP for Automated Creative Design in Digital Advertising

The demand for personalized, visually rich content in digital advertising has led to the increased adoption of AI technologies such as Generative Adversarial Networks and Natural Language Processing. Yet, finding an accurate match of textual descriptions with high-quality, contextually relevant images is still a challenge, particularly where emotions and creativity are part of the process in advertising. This paper proposes a new framework that integrates DCGANs with natural language processing techniques-sentiment analysis by Text CNN-and generates high-quality, contextually appropriate images from text descriptions for digital advertising. It utilizes the “Stable Diffusion—Image to Prompts” dataset from Kaggle, together with real and AI-generated images. Text CNN conducts sentiment analysis to capture the emotional tones in the input text. The evaluation metrics used for the model include but are not limited to Inception Score (IS), Frechet Inception Distance, and Latent Space Interpolation. The Inception Score demonstrates better performance with the proposed framework, coming to 4.0 against the score of 2.5 in AttnGAN; it has a lower FID of 80 versus 125.98 in AttnGAN, and also presents high-quality and smooth transitions in latent space interpolation. These improvements manifest better emotional alignment in generated images and creativity, outperforming the performance of traditional models. This research points out the scope of integrating GANs and NLP for automating the creation of personalized, high-quality digital ads and thereby offering a scalable solution for modern marketing needs.