A Data-Driven Approach for Educational Improvement and Quality Enhancement

Despite vast data availability, many organizations struggle to extract actionable insights, often lacking the necessary analytical tools. Sentiment analysis, or opinion mining, has proven effective for understanding user behavior and forecasting trends, particularly in social media. Building on previous research, this study applies sentiment analysis techniques to Quality Enhancement Cell (QEC) data to evaluate teachers' performance and gain insights into student perceptions. The proposed method uses machine learning algorithms to analyze sentiment and predictive patterns, offering objective support for teacher evaluation and fostering informed recommendations for educational development. The results emphasize data-driven improvements in teaching quality, highlighting sentiment analysis as a valuable tool for advancing educational outcomes based on stakeholder feedback.