Real-Time Emotion-Aware AI Web Systems Using Multimodal Inputs (Text, Voice, and Behavioral Signals)

Emotion recognition is a critical component of next-generation intelligent systems, enabling more natural and adaptive human–computer interaction. This paper presents the design and development of a Real-Time Emotion-Aware AI Web System that integrates multimodal inputs including text, voice, and user behavior. The proposed system leverages Natural Language Processing (NLP), speech emotion recognition, and behavioral analytics to detect emotional states in real time. A fusion-based deep learning approach is used to combine features from different modalities for improved accuracy. Experimental results demonstrate that multimodal systems outperform unimodal approaches in emotion detection accuracy and responsiveness, making them suitable for applications such as mental health monitoring, customer support, and adaptive user interfaces.