When assessing the efficacy of instruction, the caliber of the curriculum, and the overall academic experience, student feedback is essential. However, it is ineffective and frequently inconsistent to manually analyze vast amounts of unstructured textual feedback. In order to automatically classify student feedback into three categories—positive, neutral, and negative—this study suggests a Student Sentiment Analysis System that makes use of transformer-based deep learning techniques. The system uses self-attention mechanisms and a transformer architecture based on RoBERTa to capture contextual relationships within text. The suggested model creates contextual embeddings that interpret sentiment based on complete sentence meaning rather than isolated keywords, in contrast to conventional machine learning techniques that rely on manual feature engineering. Batch processing and real-time feedback analysis are made possible by an interactive web interface created with Streamlit. An interactive web interface developed using Streamlit enables real-time feedback analysis and batch processing. Experimental evaluation demonstrates reliable classification performance and practical applicability in educational environments. The system transforms raw textual feedback into structured sentiment insights, supporting data-driven academic decision-making.
Sentiment Analysis, Transformer Model, RoBERTa, Student Feedback, Natural Language Processing, Deep Learning.
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