A Review on Sentimental Analysis based on Amazon Reviews using NLP, Naïve Bayes, Random Forest and Text Blob

The project explores sentiment analysis on Amazon product reviews using various machinelearning and NLP models. The study involves data collection, preprocessing, and model training to classifysentiment polarity. Naïve Bayes and Random Forest classifiers, along with TextBlob sentiment analyzer,are evaluated based on precision, recall, and F1-score. The project uses flask to connect front and backendand HTML, CSS based frontend for practical implementation. The results highlight that Naïve Bayes andRandom Forest outperform TextBlob in accuracy. The study provides a structured approach to sentimentclassification, offering valuable insights for businesses and consumers.