EMOTIONALLY INTELLIGENT AI: IMPROVING HUMAN-COMPUTER INTERACTION WITH NLP AND SENTIMENT ANALYSIS
Keywords:
Human computer interaction (HCI), Natural language processing (NLP), Sentiment analysis, Emotion recognition, Empathetic response generation, Transformer model, Ulti modal emotion analysis, Contest aware AIAbstract
This study explores the development of emotionally intelligent AI systems that enhance human-computer interaction by recognizing and responding to user emotions using NLP and sentiment analysis. Unlike traditional AI tools with fixed responses, the proposed platform adapts its replies based on real-time emotion recognition, employing a transformer-based generative model that achieves 92% accuracy in detecting emotions. Evaluated for coherence, empathy, and relevance, the system produces realistic and empathetic conversations, marking a significant advancement in AI agents. Applications span customer service, healthcare, and psychological support, where the AI can detect frustration or stress and respond appropriately to improve user experience and trust. Ethical management of emotional data remains a priority as such systems become widespread. Future research will focus on detecting complex emotions, incorporating multimodal inputs, and enhancing intercultural communication. This work paves the way for more human-centric, empathetic AI, bridging effective task performance with meaningful emotional engagement.
