Social Media Leveraging Data Analytics to Enhance User-Experience

In the present computerized innovation period, it is significant for business endeavors to assemble a serious advantage to clients through distinctive administrations or responsiveness to dynamic business circumstance. Information or data like shopper opinions, experiences and sentiments on various product and services produced from various Social Media platform or sites helps to improve value of business organizations. Gathering data from Social Media (SM) sites and evaluating that data to make business decisions is called Social Media Analytics (SMA). SMA has been getting importance in the recent times due to 3.5 billion active users’ presence in Facebook, Instagram, Twitter, LinkedIn etc. An Integrated SMA framework based on Multi-Criteria Decision Making (MCDM) models and Machine Learning methods has been implemented. Machine Learning (ML) methods such as Naive Bayes, K-nearest neighbors (KNN), Decision Trees and Support Vector Machines (SVM) has been applied to classify data through Social Media Analytics operation using MCDM model based methods such as Analytical Hierarchy Process (AHP), Statistical Design Institute (SDI) Matrix method. The test results obviously exhibit that MCDM model systems alongside ML calculations successfully perform SMA activity