Authors: Shravani Phalke, Rajit Joshi, Raj Lohar, Bharti Dhote
Abstract: By facilitating natural, flexible, and context-aware communication across a variety of languages and cultural contexts, artificial intelligence (AI) has revolutionized human-computer interaction. Large language models have advanced, but chatbots still have difficulty identifying, interpreting, and reacting sympathetically to users' emotional states. As a result, they frequently provide generic responses that lack genuine resonance. This paper introduces Novachat, a full-stack AI chatbot designed to close this gap by combining multilingualism and sophisticated emotion intelligence into a scalable MERN-stack architecture. In order to provide human-like, contextually nuanced conversations in English, Hindi, Marathi, and other languages, Novachat's modular framework integrates sentiment analysis, emotion-adaptive response generation, and language detection. To ensure smooth real-time adaptability, each module functions as a microservice and communicates via orchestration driven by APIs. The study describes the system's overall architecture, emotional classification model, dataset organization, and quantitative performance assessment using metrics like System Usability Scale (SUS), emotion recognition accuracy, response relevancy, and user engagement latency. According to experimental results, Novachat generates sympathetic responses and detects emotions with high accuracy; a SUS score indicates strong user acceptance. The field is moving closer to AI systems that genuinely recognize and value the user's emotional experience as a result of these results, which validate Novachat's function as an efficient, inclusive, and emotionally engaging conversational platform.
