Aspect Opinion Mining on Customer Reviews

e-commerce is becoming more and more popular and interactive. Merchants selling products on the Web 2.0 often ask their customers to review the products which they have purchased. The number of reviews can be in hundreds or even thousands for a popular product. This makes it difficult for a potential customer to read them or to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track of customer opinions. In this research, we aim to mine and to analyze all the customer reviews of a product. The algorithm proposed in this research is more reliable on opinion justice because it is unsupervised and the accuracy of the result improves as the number of reviews increases. Our research is performed in three steps: (I) mining the aspects and opinions of a product that have been commented on by customers: (2) identifying opinion words in each review and deciding whether each opinion word is positive, negative or neutral; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques.