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The escalation of online shopping trends, social networks, and blogs makes shopping customers to know premier and quality product from customer reviews posted on social networks. Also, the business enterprise can perceive customers like opinion, demand, and expectation in real time. Customers are expressing their opinion, feedback, and emotions of online products through social networks and blogs. The problem focused here is, from customer point of view, identifying trendy and quality product for online purchasing, enterprise seeking customer’s opinion, brand reputation based on the reviews in social networks and blogs. The main problem is, for a single product, customer’s opinions are vast and vary in different social networks (Twitter, Facebook) and blogs (Amazon, Flipkart). This problem is addressed in this work for extracting useful hidden information by integrating the reviews of social networks and reviews in blogs. The proposed work on opinion mining has developed a new technique with a set of rules for combining social network reviews (Twitter reviews, Facebook reviews) and blog reviews (Amazon reviews). Then the reviews are mined to provide crisp information of reviewer opinion on products to new customers and organization. The new information will assist customer to make a wise decision for buying products. With the extracted information, the enterprises can read the customer's mind to improve their business by offering to customer trending products. The proposed model has achieved more than 80% accuracy and f1-score in sentiment prediction. The result has efficiently proved the performance of the model.

Opinion Mining on Integrated Social Networks and E-Commerce Blog