Abstract
Several challenges accompanied the growth of online social networks, such as grouping people with similar interest. Grouping like-minded people is of a high importance. Indeed, it leads to many applications like link prediction and friend or product suggestion, and explains various social phenomenon. In this paper, we present two methods of grouping like-minded people based on their textual posts. Compared to three baseline methods K-Means, LDA and the Scalable Multi-stage Clustering algorithm (SMSC), our algorithms achieves relative improvements on two corpora of tweets.