SpireMetrics: An Integrated Approach for Student Feedback Analysis and Sentiment Prediction

SpireMetrics, a web-based platform, facilitates the collection and analysis of student feedback on current semester subjects, assessing faculty performance in areas like subject knowledge and doubt resolution. Ensuring anonymity, the system promotes honest feedback and features secure logins for administrators and faculty. Administrators create feedback forms specifying subjects and faculty, while faculty access and review feedback, enhancing transparency. Angular and Chart.js enable data visualization, offering a comprehensive view. Additionally, machine learning, specifically the BERT, Bidirectional Encoder Representations from Transformers model, is integrated to predict sentiment in the feedback, distinguishing between positive and negative responses. This study explores the SpireMetrics architecture, implementation, and outcomes, highlighting the usefulness of the tool for educational institutions. The findings reveal the project's potential to improve the educational experience and the accuracy of sentiment analysis. This research paves way for future developments in educational feedback systems and sentiment analysis in academic settings.