AI-DRIVEN SENTIMENT ANALYSIS

Efficient complaint management is essential for enhancing student satisfaction and institutional accountability in higher education. However, most existing systems lack intelligence, scalability, and analytical depth. This dissertation presents the design and implementation of an AI-driven Student Complaints Management System (SCMS) that integrates Natural Language Processing (NLP) and sentiment analysis to improve how student grievances are captured, categorized, and resolved. The system applies text-mining techniques to interpret complaint narratives, classify issues by department, and assess emotional tone to prioritize responses. A modular web-based architecture enables real-time reporting, trend visualization, and administrative insights. Using a design science research methodology, the study demonstrates that intelligent automation significantly reduces resolution time and enhances decision-making transparency. The proposed SCMS contributes to the field of educational technology by merging human-computer interaction with data analytics to foster responsive governance and continuous improvement in higher education service delivery