Sentiment Analysis of Movie Reviews using Machine Learning and BERT

Developed a Natural Language Processing (NLP) system to classify movie reviews as positive or negative using the IMDb dataset of 50,000 reviews.

The project implements a complete NLP pipeline including text preprocessing, feature extraction using TF-IDF, and training multiple machine learning models such as Logistic Regression, SVM, Naive Bayes, and Random Forest.

To compare traditional approaches with modern deep learning techniques, a BERT transformer model (bert-base-uncased) was fine-tuned for sentiment classification.

The project analyzes the performance trade-offs between classical machine learning models and transformer-based models in terms of accuracy, efficiency, and computational cost.