The rapid increase in internet users along with growing power of online review sites and social media has given birth to Sentiment analysis or Opinion mining, which aims at determining what other people think and comment. Nowadays, several websites are available on which a variety of products are advertised and sold. Prior to making a purchase an online shopper typically browses through several similar products of different brands before reaching a final decision. This seemingly simple information retrieval task actually involves a lot of feature-wise comparisons and decision making, especially since all manufacturers advertise similar features and competitive prices for most products. The proposed system presents a semi-supervised approach for mining online user reviews to generate comparative feature-based statistical summaries that can guide a user in making an online purchase. In this system sentiment analysis of product reviews gives us not only positive and negative reviews but also gives neutral and constructive opinion where the system can suggest some improvement about the product also the result is represented in the graphical and tabular method. 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. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online to demonstrate the effectiveness of the techniques.