Emotion-Aware Multimedia Synthesis: A Generative AI Framework for Personalized Content Generation based on User Sentiment Analysis

This research work introduces an innovative approach to multimedia content creation by incorporating emotion and sentiment analysis into a Generative Adversarial Network (GAN) framework. The system dynamically detects and interprets the user's emotional and sentiment cues, allowing for real-time adaptation and generation of multimedia content, including images, videos, and music. Leveraging advanced deep learning techniques, such as sentiment-aware GANs and emotion recognition through neural networks, the proposed framework establishes a seamless connection between user expression and media synthesis. By conditioning the generative process on the user's emotional state, the model learns to generate contextually relevant and emotionally resonant content. This research work encompasses an in-depth analysis of existing emotion recognition methods, the design and architecture of the proposed system, hardware and software requirements, as well as rigorous testing and performance evaluations. The outcome aims to redefine interactive multimedia experiences, empowering users to effortlessly communicate and translate the emotions into personalized and expressive digital content