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 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. It results in evaluating their performance in terms of parameter usage and contributions. The paper also discusses their experimental results and challenges found. Therefore, this study shows the maximum usage of feature extraction, positive-negative sentiment, Amazon web source, mobile phone for a large set of reviews in the existing algorithms.