Published Paper Details:

As online shopping becomes increasingly more popular, many shopping web sites encourage existing customers to add reviews of products purchased. These reviews make an impact on the purchasing decisions of potential customers. At Amazon.com for instance, some products receive hundreds of reviews. It is overwhelming and time restrictive for most customers to read, com-pretend and make decisions based on all of these re-views. Customer�s most likely end up reading only a small fraction of the reviews usually in the order which they are presented on the product page. The fundamental objective of this paper is to give nearly full picture of Classifying Amazon Reviews, its sorts and characterization. It also includes the complex orders of late articles and the outline of the ongoing pattern of research in the Classifying Amazon Reviews and its related territories Hence, our application eases this task by analysing and summarizing all reviews and predicting rankings for reviews .reviews provide objective feedback to a product and are therefore inherently useful for consumers. These ratings are often summarized by a numerical rating, or the number of stars. Of course there is more value in the actual text itself than the quantified stars. And at times, the given rating does not truly convey the experience of the product � the heart of the feedback is actually in the text itself. The goal therefore is to build a classifier that would understand the essence of a piece of review and assign it the most appropriate rating based on the meaning of the text, which will help the user decide what other buyers have experienced on buying this product. We carry out this process by a number of modules that include feature extraction and opinion extraction which improves the process of analysis and helps in the formation of an efficient summary.