In recent years, the retail market industry has taken a broad form to sell the products online and also to give the opportunity to customers to provide their valuable feedbacks, suggestions and recommendations. The opinion summarization and classification systems extract and identify a range of opinions about different online available products in a large text-based review set. This paper addresses and reviews the concepts of automatic identification of the sentiments expressed in the English text for Amazon and Flipkart products using Naive Bayes, Logistic Regression, SentiWordNet, Random Forest and K-Nearest Neighbor techniques. It presents a detailed comparative study of such existing sentiment analysis algorithms and methodologies on the basis of five key parameters. The paper also proposed Product Comment Summarizer and Analyzer (PCSA) system. It is automatic and generic comment analyzer which can find the polarity of the sentiments and comments very effectively. It summarizes the comments and classifies them into the pre-defined positive, negative or neutral classes. It results in evaluating their performance in terms of parameter rating, classifiers, accuracy.