The focus of this paper was on Amazon product reviews. The goal of this is to study is two (NLP) for evaluating Amazon product review sentiment analysis. Customers can learn about a product's quality by reading reviews. Several product review characteristics, such as quality, time of evaluation, material in terms of product lifespan and excellent client feedback from the past, will have an impact on product rankings. Manual interventions are required to analyse these reviews, which are not only time consuming but also prone to errors. As a result, automatic models and procedures are required to effectively manage product reviews. (NLP) is the most practical method for training a neural network in this era of artificial intelligence. First, the Naive Bayes classifier was used to analyse the sentiment of consumer in this study. The (SVM) has categorizeduser sentiments into binary categories. The goal of the approach is to forecast some of the most important characteristics of an amazon-based product reviews, and then analyse Customer attitudes about these aspects. The suggested model is validated using a large-scale real-world dataset gathered specifically for this purpose. The dataset is made up of thousands of manually annotated product reviews gathered from amazon. After passing the input via the network model, (TF) and (IDF) pre-processing methods were used to evaluate the feature. The outcomes precision, recall and F1 score are very promising