A new model for making valuable decisions through user social network profiles and insights

Abstract

Recently so many users who are different in power/interest trade news on social networking sites like Arabic Twitter and share their views on current affairs. These opinions/comments can’t be used to make good decisions or boost output in a particular area without differentiating users according to their closeness/interest to this area. This paper’s major goal is to present a generalized automatic model to analyze user opinions to make valuable decisions in a particular area based on the degree of user closeness/interest to this area. The proposed model combines Rough set theory, Mendelow’s power-interest model, and data mining decision-making techniques. Rough set theory based on Mendelow’s power-interest model supports the identification and classification of users by their account features. Unsupervised k-means would then be used to cluster their replies/opinions into positive, negative, or neutral. The result generated from the classification of users and the clustering phase of Arabic replies/opinions supports the making of valuable/important decisions in a particular area. A case study is carried out to demonstrate the effectiveness and accuracy of the proposed model.