IMDB Movie Review Sentiment Analysis Built

IMDB Movie Review Sentiment Analysis

Built an end-to-end Natural Language Processing system to classify 50,000 IMDB movie reviews as positive or negative.

The project includes a complete text preprocessing pipeline with tokenization, stop-word handling, POS-aware lemmatization, and negation preservation. I used TF-IDF vectorization with unigram and bigram features and trained and compared four machine learning models: Logistic Regression, SVM, Naive Bayes, and KNN.

The best-performing models achieved approximately 90% accuracy, with SVM and Logistic Regression producing the strongest results. I also built a Streamlit web application that allows users to enter reviews, select a trained model, and receive real-time sentiment predictions with confidence scores.

Key work:

Developed an end-to-end NLP preprocessing pipeline

Implemented TF-IDF feature extraction with n-grams

Trained and compared four classification algorithms

Evaluated models using accuracy, precision, recall, confusion matrices, and classification reports

Serialized trained models for deployment

Built a real-time Streamlit inference application