Improving product rating system with text mining on product review/comments

Abstract

It is often considered a better exercise to have a complete idea of a particular service or a product before availing it. Now a days almost every online shopping sites or even the manufacturer of the product has a star based rating system and review/comment zone in their website. It is often not feasible to go through all the review before purchasing or availing that particular product. So people often tends to have an idea based on the number of stars on that product.Currently available systems use a star based rating where people rate the service or product on the scale of 5 or 10. The problem with that is when they give those rating stars they often tends to give it without giving much thought to it. User experience level and his mind set while rating varies very much. For example a person who loves particular brand of Soda, if he drink soda of another brand he might rate it lower than what it should be because he is used to a particular brand. But when he writes a review the chances are higher that he will write the major positive and negative aspects of that product . Thus the chances of getting a better feedback comes when it is review rather than stars. But as it has been mentioned earlier, going through all the reviews are not feasible. So we tried to improve the rating system by extracting information from the review text by using text mining technique upon that.