Using AI in Analysing Satirical Drawings in Commercial Advertisements

Satirical drawings have served as an effective instrument in commercial ads, where humor and irony can help mold public perception. Yet, satirical content can be subjective and hard to recognize in an automated way or to analyze. This study investigates using convolutional neural networks (CNNs) to analyze satirical reconnaissance in commercial advertisements through a multimodal image and text processing analysis. The CNN model effectively identifies and classifies satirical content with high accuracy by leveraging deep learning and natural language processing techniques. Empirical results show that multimodal CNN could achieve much better classification performance than single-mode models, achieving a 91 of AUC-ROC. The study notes the use of AI in content moderation, ad optimization, and audience engagement tactics. Despite prevalent issues with deciphering elaborate humor, this groundbreaking study offers an initial structure for automated recognition of satirical content, setting the stage for more profound proficiency in computational humor analysis as well as advertising intelligence.