Twinkle Soni, Dr. Vinod, Deepali Verma
The rapid development of social media platforms has revolutionized the field of advertising and made it a highly personalized, image-oriented, and algorithmically-driven process. This experiment will look into the relationship between algorithmic emotion and influencer imagery and how they will produce an effect on consumer perception within the digital advertising environment. The hypothesis of this research is a multimodal analysis based on the extraction of the visual characteristics of the content, sentiment analysis, and engagement rates to determine to what extent the emotionally resounding influencer content is boosted by the platform algorithms. The data obtained on prominent social media platforms, with the examples of Instagram, Tik Tok, and YouTube, are processed with the help of deep learning algorithms in the form of convolutional neural networks to process the image and natural language processing to detect the sentiment. The results indicate that the content of influencers that is aesthetically pleasing and has a positive emotional impact on the user has a huge boost on user engagement and perception. Platform analysis provides insights into how short-form content platform is more engaging since it is more immersive and algorithm-driven. The study underlines the importance of integrating emotional intelligence and using visual stories as the part of the digital marketing strategy and the need to address the ethical concerns related to the manipulations in algorithms. Overall, the proposed framework can be highly beneficial to marketers, researchers, and the creators of the platforms to optimize consumer engagements in social media advertising.