Emotion-Aware Virtual Assistant: Integrating Machine Learning with Web Technologies

Abstract

Human beings express emotions through gestures such as facial expressions, hand movements, and even text grammar. Emotion recognition systems serve as a crucial component of Human-Computer Interaction (HCI), enabling computers to interpret and respond to human emotions effectively. This study proposes a facial expression recognition system using deep learning models to classify emotions into seven categories: happy, sad, angry, disgusted, neutral, fearful, and surprised. The proposed method utilizes Python-based deep learning libraries, incorporating feature detection algorithms to analyze facial expressions. By capturing images from video sequences, the system identifies patterns of emotional changes over time. A key feature of this approach is the creation of a real-time dashboard that visualizes these emotional trends, allowing for a more comprehensive understanding of the user’s emotional state. Beyond facial recognition, the system integrates a conversational AI component to engage users in meaningful interactions. By analyzing both facial expressions and text-based responses, the virtual assistant can provide more accurate emotional assessments. The final result is based on the combined analysis of visual and conversational data, ensuring a more holistic interpretation of user emotions. This study aims to enhance the accuracy of emotion recognition systems by leveraging deep learning techniques and real-time data processing. Such an emotion-aware virtual assistant can have significant applications in various fields, including mental health monitoring, customer service enhancement, and personalized user experiences in digital applications.