Enhancing faculty evaluation through NLP-based sentiment analysis of student feedback

Faculty performance evaluation is crucial for maintaining high teaching standards in academic institutions. Traditional feedback mechanisms often rely on numerical ratings or manually reviewed responses, which lack depth in capturing student sentiments. This paper presents an NLP-powered sentiment analysis system that extracts meaningful insights from textual student feedback. The system classifies sentiments to highlight faculty strengths and areas for improvement. The analysis results are visualized through interactive dashboards, enabling faculty to track their performance trends and administrators to manage faculty data efficiently. By automating sentiment analysis and integrating data-driven feedback loops, this system fosters continuous faculty development and enhances the overall academic environment.