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 keywords of each member according to the relevance of their tweets for the clique. The keyword weight is influenced by two factors. The first factor is calculated based on the number of connections of a user within the clique, and the second factor depends on whether the user publishes personal information or information of general interest. We developed the prototype of a Twitter client with clique management functionality and conducted an experiment in which on average 46.96% of the keywords extracted from our proposed method were relevant for the cliques as opposed to 38.31% for the baseline method.