Sentiment Polarity Prediction for Amazon Product Reviews Using Machine Learning and Deep Learning

The exponential growth of e-commerce platforms, particularly Amazon, has resulted in a massive volume of customergenerated reviews, making manual sentiment analysis both time-consuming and inefficient. This study proposes an automated system to predict the sentiment polarity (positive or negative) of Amazon product reviews using a combination of machine learning and deep learning techniques. The reviews are processed and converted into vectorized representations using methods such as Bag of Words (Bow), TF-IDF, Word2Vec, and their respective unigrams, bigram and weighted variants. A variety of classifiers—including Naive Bayes, Logistic Regression, Support Vector Machines (SVM), Random Forest, and Long ShortTerm Memory (LSTM) networks—are trained and evaluated. Experimental results indicate that Logistic Regression with bigram TF-IDF achieves the highest test AUC of 0.9615, demonstrating superior generalization. While LSTM models show potential in capturing sequential dependencies, they require further optimization. The study emphasizes the importance of automated sentiment classification and outlines future directions such as multilingual analysis and real-time deployment using cloud platforms.