AI Driven Mood Detection for Personalized Content Delivery

Abstract

Human emotions significantly influence the way individuals perceive, process, and engage with digital content. Traditional content delivery systems often overlook the user’s emotional context, leading to reduced engagement and limited personalization. This research proposes an AI-driven framework for mood detection and personalized content delivery by integrating deep learning and Generative AI. The system employs Convolutional Neural Networks (CNNs) for facial expression analysis, speech emotion recognition models for voice tone analysis, and Natural Language Processing (NLP) models for sentiment detection from text. A multimodal fusion strategy is adopted to achieve robust mood classification across diverse input sources. Once the emotional state is inferred, a Generative AI module recommends or generates personalized content tailored to the detected mood, ensuring improved relevance and user satisfaction. The proposed approach aims to advance human-computer interaction by providing an adaptive, emotion-aware content delivery system that can be applied in domains such as digital media, education, healthcare, and entertainment.