Senti Guide: A Machine Learning-Based Sentiment Analysis System for Student Feedback Evaluation

Abstract

In academic settings, student feedback plays a crucial role in evaluating events and improving future planning. However, traditional manual feedback processing is time-consuming, error-prone, and inconsistent, often leading to delays in decision-making. To address this, the Senti Guide web application was developed, incorporating sentiment analysis to automatically classify student feedback into positive, neutral, or negative sentiments. The system utilizes Support Vector Machines (SVM) with a Radial Basis Function (RBF) kernel for sentiment classification, ensuring efficient handling of nonlinear relationships in the data. This method allows the system to effectively differentiate between complex patterns in student feedback. Preprocessing steps such as normalization, tokenization, and stop-word removal are applied to clean and structure the feedback data, preparing it for optimal analysis. Furthermore, hyperparameter tuning is employed to optimize the model’s performance, improving its accuracy, precision, recall, and F1 score. The model demonstrates significant performance improvements, achieving high classification accuracy of 90.05%, precision of 90.56%, recall of 90.05%, and F1-score of 90.04%. By automating sentiment analysis, Senti Guide provides the SSG with a scalable solution to modernize evaluation methods and enhance the future quality of school events. This research underscored the significance of employing machine learning to improve feedback processing, offering valuable insights for the Supreme Student Government to enhance event planning and student engagement.