SENTIMENT ANALYSIS OF MOVIE REVIEWS

This project involved engineering a sentiment analysis system to classify and rank user reviews using variations of the Naïve Bayes model, achieving a remarkable 97.26% accuracy with the Multinomial Naïve Bayes approach. The solution incorporated NLTK’s Sentiment Intensity Analyzer for precise sentiment extraction and included robust data preprocessing and visualization. This work highlights expertise in natural language processing (NLP), machine learning, and the ability to derive actionable insights from textual data.