Multi-modal Emotion Detection System

Emotions play a crucial role in human communication and decision-making. With the rise of human-computer interaction (HCI) systems, there is an increasing need to develop more accurate and reliable emotion detection mechanisms. While traditional emotion detection systems rely on a single modality, such as facial expression or text, the integration of multiple modalities can significantly improve detection accuracy. This research proposes a Multi-Modal Emotion Detection System that uses facial expressions analyzed by Convolutional Neural Networks (CNNs) and textual data processed using the Natural Language Toolkit (NLTK). This hybrid approach enables more precise and comprehensive emotional analysis by fusing visual and textual data. The system is evaluated on accuracy, precision, recall, and F1-score to demonstrate its potential applications in real-time emotion recognition across various domains such as mental health, HCI, and personalized user experiences.