Emotion-Aware Chatbot Architecture: Enhancing Human-Robot Interaction through Sentiment Detection and Lip Sync

Abstract

This case study explores the development of an emotionally responsive chatbot system that integrates sentiment-driven facial expressions with real-time lip synchronization, resulting in a visually expressive and emotionally adaptive virtual agent. The system was evaluated through controlled interactions and performance testing, focusing on sentiment detection accuracy, visual responsiveness, and synchronization quality. Sentiment analysis achieved an average classification accuracy exceeding 85%, with particularly strong results in detecting emotional extremes such as delight and frustration. Corresponding facial expressions, especially through animated eye movements, were reported by users as appropriate and contextually relevant. The lip synchronization mechanism, which maps phonemes to visemes in real time, maintained a latency of approximately 50 milliseconds, significantly enhancing realism and user engagement. These integrated features contributed to improved clarity, empathy, and naturalness in chatbot-human interaction. Additionally, the system offers accessibility benefits for users with hearing impairments or those needing stronger emotional cues. By combining visual, verbal, and emotional channels, the chatbot transcends traditional dialogue systems to become a more relatable and socially aware digital companion.