SOCIAL MEDIA E-CUSTOMERS’ BEHAVIOUR MINING

As nowadays, Social media has become an important source of information, where people can express their opinions and their views. To analyze social media data of e-customer's there is need to extract information for making predictions of how they behaves during shopping in an e-commerce market place. After collecting data from an e-commerce market, performed a data mining application for extracting about how customers' behaves online whether to buy product or not. The model which is presented predicts whether customers will not or will buy their items or products added to shopping baskets on a market place. As there is massive growth of online social networks (OSN) like Twitter, Facebook and other social networking portals have created a need to determine people's opinion and moods. Posting user feedback on products has become increasingly popular for people to express their opinions toward products and services. The companies think that there is a chance for an improvement in market for a product to people to aware and feel about it. In this study, there is use of sentiment dictionary as an Affine for tokenization and preprocessing process and also there is use of machine learning techniques to find about e-commerce site that is more useful and good for e-customers by predicting and analyzing through their reviews. “