Online social networks have spawned myriads of online social groups, where people can interact and exchange their ideas. However, the major issues that interfere with the user security and comfort are privacy breach, groups without opt-in options, clutter created out of numerous groups in which a user is a member of and difficulty in managing group principles. This can be lessened to an extent by an automated filtering mechanism capable of categorizing members within a group based on their pattern of response. In our proposed method, the posts within a group are clustered based on stylistic, thematic, emotional, sentimental, and psycholinguistic aspects. Then the members of the group are categorized based on their response to the posts belonging to different aspects as mentioned above. This results in categories of individuals within a group, who are like minded. The categorization caters to the most important issues related to soft security , such as the clutter associated with irrelevant notifications received from multiple groups, by suggesting the users, posts that are likely to be of interest to them. It also helps to identify the group members intended towards spreading posts that violate group policies. The categorization exhibits satisfiable performance in case of large number of candidate members in a populous group by performing clustering based on linguistic features. The double level of clustering, based on the posts and response of users based on the aspects of the posts, enhances the performance of the system, hence outperforming traditional recommender systems. The system has been tested on Facebook group data, where it offers a significant solution to an unaddressed problem associated with social networking groups.