A Review on Automatic News Classification using the Probabilistic Classification Algorithms

Reviews are unbiased information obtained from the sources outside an organization, which makes them more reliable in the eyes of customers. Online shoppers are very much concerned about product reviews before making any decision regarding buying the product. Product reviews plays an important role in determining what kind of product is. Such reviews provide useful information about customer concern and their experience with the product. Consequently, these reviews will be helpful for a business making products for the purpose of product recommendation, better customer understanding and attracting more loyal customers. As ecommerce has become so popular, numbers of reviews are increasing day by day. It is difficult for a customer to read all the reviews manually. In this paper, an approach is developed which is used to obtain the summary from thousands or hundreds of online reviews. This approach uses extraction summarization for summarizing the reviews thereby selecting the original sentences and putting it together into a new shorter text explaining the overall opinion about the product. Although previous studies of deriving useful information from customer reviews focus on categorical or numerical data and textual data has been ignored. But textual data are of equal importance so it should not be ignored. So, this approach includes every aspect of the review in the summary so that a customer would be able to make a right decision regarding product.