In today’s marketplace, online evaluations are essential for improving consumer communications worldwide and affecting consumer purchasing behavior. Giants in the e-commerce space like Amazon, Flipkart, Myntra, Ajio, eBay and others provide customers a forum to discuss their experiences and give potential consumers accurate information on how well a product works. The task of accurately predicting how consumers feel through analysis of their evaluations has proven to be challenging and time-consuming because of the large amounts of data that are collected, the lack of established standards, and the random variables that arise from the language that consumers use. This review paper examines client sentiment toward a certain product through analysis of sentiment polarity determination and classification (positive, negative, and neutral). The paper discusses the computational steps, approaches, and available datasets in the field of sentiment analysis.