Online Shopping Product Aspect and Ranking Using Support Vector Machine Algorithm

The peoples are before the purchasing invention to see the product reviews on internet. But some time the reviews are often not confidentiality and provide difficult about product aspect and people could not identify the review information via internet. Nowadays online shopping plays an excellent role in our life. Peoples are more comfortable to buy online products at the same time manufacturers also provide reliable products .Most retail Websites promotes consumers to write their feedbacks about products to express their opinions on various aspects of the products. The web contains outstanding source of consumer opinions. A product may have thousands of aspects. Different kinds of users give their different kinds of opinions .so the volume of the textual information increased rapidly. It is difficult for users to read all the reviews to make a good decision. It is also difficult for manufacturers and providers. These needs extracted aspects and estimated ratings clearly provide more detailed information of users to make decisions and for suppliers to monitor their customers. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps. (1) Mining product features that have been commented on by customers. (2)Identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative. (3) Summarizing the results.