Author(s) :

R.Uma Aruna Devi.

Volume/Issue :

Abstract :

The ability to recognize and respond to user emotions is critical for enhancing human-computer interactions, particularly in applications such as customer support, therapy, and education. Traditional Chabot’s often lack emotional intelligence, leading to impersonal and ineffective conversations. This paper proposes a novel approach to developing emotionally intelligent Chabot’s using advanced generative AI models. By leveraging natural language processing (NLP) and deep learning techniques, the Chabot system can detect emotional cues from text, such as tone, sentiment, and context, enabling it to generate empathetic and contextually appropriate responses. The model is trained on diverse conversational datasets, incorporating emotional tagging and language patterns that reflect different moods and scenarios. A transformer-based architecture, like GPT (Generative Pre-trained Transformer), allows the Chabot to produce coherent, emotionally relevant dialogues while adapting its tone based on real-time analysis of user input. Experimental results indicate that the emotionally intelligent Chabot improves user satisfaction, engagement, and rapport-building compared to conventional bots. This study highlights the potential of generative AI to create Chabot’s that can effectively understand and respond to human emotions, providing meaningful, human-like interactions.

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