Artificial intelligence powered group discussion analyzer using natural language processing and sentiment analysis

Group discussions play a vital role in academic assessments, recruitment processes, and professional skill evaluation, as they effectively measure communication ability, critical thinking, teamwork, and leadership skills. However, traditional group discussion preparation methods suffer from several limitations, including difficulty in forming practice groups, lack of objective feedback, high training costs, and inconsistent evaluation standards. To address these challenges, this paper proposes an AI-Powered Group Discussion Analyzer, a web-based intelligent platform that integrates Artificial Intelligence, Natural Language Processing (NLP), real-time speech-to-text processing, and sentiment analysis to enable structured and objective group discussion practice and evaluation. The proposed system allows users to participate in discussions with either human participants or AI-driven virtual participants, ensuring continuous practice availability. Developed using React.js for the frontend, Node.js with Express for the backend, and MongoDB for data storage, the platform supports real-time audio capture, transcription, speaker identification, and linguistic analysis. Advanced AI models analyze topic relevance, participation levels, sentiment trends, and communication patterns, and generate comprehensive post-session performance reports highlighting individual strengths, weaknesses, engagement metrics, and personalized improvement recommendations. Experimental results demonstrate reliable transcription accuracy, effective sentiment classification, and accurate participation analysis, confirming the system’s practicality and scalability. By reducing subjectivity and enhancing accessibility, the proposed approach provides data-driven feedback and offers an efficient solution for communication skill development in academic, recruitment, and professional training environments.