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In Twitter and other microblogging services, users often have large social networks formed around cliques (communities) such as friends, coworkers or former classmates. However, the membership of each user in multiple cliques makes it difficult to process information and interact with other clique members. We address this problem by automatically dividing the social network of a Twitter user into personal cliques and assigning keywords to each clique to identify the common ground of its members. In this way, the user can understand the structure of their social network and interact with the members of each clique independently. Our proposed method improves clique annotation by not only extracting keywords from the tweet history of the clique members, but individually weighting the extracted