By keeping and gaining customers, every small and large firm hopes to increase earnings and sales. It is vital to estimate future sales in order to improve sales. Aim of this work is to find the product is liked and recommended by the customer or not. To retain customers, product sellers must assess online customer demand in order to manage product, brand, and inventory levels. To improve customer service, online retailers or sellers must first understand the needs of their customers. It's also important to think about future sales. The proposed opinion mining on Amazon products is based on various sorts of real-time data obtained from Twitter. Lexical features, sentiment features and pragmatic features have also been used to improve the classification of review opinion features. After preprocessed the collected real time reviews have been involved in the process of feature extraction. Finally positive and negative opinions about the Amazon products have been classified by the proposed classification model. The proposed work findings and results states whether the Amazon shopping customers likes and recommend the product or not recommend. If the positive opinionated reviews are higher, then the customers like the product and recommend the product to purchase. If the negative opinionated reviews are higher, then the customers not like the product and not recommend the product to purchase. This research aims to aid clients in making purchasing decisions.