BERT-Driven Sentiment Analysis: Automated Course Feedback Classification and Ratings

Automating the analysis of unstructured textual data, such as student course feedback, is crucial for gaining actionable insights. This project focuses on developing a sentiment analysis system leveraging the DeBERTa-v3-base model, a variant of BERT (Bidirectional Encoder Representations from Transformers), to classify feedback sentiments and generate corresponding ratings on a 1-to-5 scale.

A dataset of 100,000+ student reviews was preprocessed and fine-tuned on the model to handle class imbalances and capture contextual nuances. Training was conducted on high-performance A100 GPUs, which enhanced computational efficiency and reduced training times significantly. The trained BERT sentiment model demonstrated superior performance compared to traditional machine learning models, achieving ~82% accuracy in sentiment classification.

The model was seamlessly integrated into a functional web application, providing a streamlined approach to evaluate and visualize course reviews dynamically. Key features include a course ratings dashboard, allowing students to view aggregated ratings for each course, and a review submission functionality where new feedback is analyzed for sentiment in real-time. For the department, an admin page provides secure access to detailed analytics, such as the distribution of positive and negative reviews, visualized trends, and the access to view individual course reviews with their corresponding sentiment scores.

This project includes a comprehensive pipeline, starting from data preprocessing and model training to deploying an end-to-end application. Traditional machine learning models, such as Logistic Regression and Decision Tree, were initially tested but yielded suboptimal results. The adoption of BERT, trained on a large dataset of 100k reviews, significantly improved performance, showcasing the benefits of advanced transformer-based models for sentiment analysis tasks.