An improved social media analysis on three layers: A real time enhanced recommendation system

Abstract

The Internet can be considered as an open field for expression regarding products, politics, ideas, and people. Those expressive interactions generate a large amount of data pinned per users and groups. In that scope, Big data along with various technologies, such as social media, cloud computing, and machine learning can be used as a toolbox to make sense of the data and give the opportunity to generate efficient analysis and studies of the individuals and crowds regarding market orientation, politics, and industry. The recommendation system for this acts as the pillars of technology, in the field of sentiment analysis and predictive analysis to make sense of user's data. However, this complex operation comes at the price of this. To each analysis, there is a personalized architecture and tool. In this paper, a novel design of a recommender system is provided powered by sentiment analysis and predictive models applied onto an example of data flow from the social media Twitter.