Abstract
Grouping like-minded users is one of the emerging problems in Social Network Analysis. Indeed, it gives a good idea about group formation and social network evolution. Also, it explains various social phenomena and leads to many applications, such as friends suggestion and collaborative filtering. In this paper, we introduce a novel unsupervised method for grouping like-minded users within social networks. Such a method detects groups of users sharing the same interest centers and having similar opinions. In fact, the proposed method is based on extracting the interest centers and retrieving the polarities from the user's textual posts. We validate our results by employing multiple clustering evaluation measures (recall, precision, F-score and Rand-Index). We compare our algorithm to a number of other clustering algorithms and opinion detection API. Results prove that the algorithm presented is efficient.