Linguistic-Based and user-Based Recommending Posts using Two-Level Clustering Methods

Abstract

Online social networks have produced bunches of online social groups where individuals can collaborate and shuffle their thoughts. In spite of, the real problems that conflicts with the user security and convenience are confidentiality break, groups without inception, confusion created from various groups of which user is a member of and difficulty in moderating groups. This can be moderated to an extent by an automated filtering method required to categorizing group members based on their response patterns. This paper proposes clustering of group posts on stylistic, thematic, emotional, sentimental and psycholinguistic methods and members of the group are categorized based on their responses to the posts belonging to different clustering methods. The categorization affords security just like the conflict associated with irrelevant notifications received from more than one groups, via recommending the users, posts which might be probable to be of interest to them. It also helps to identify the group members meant closer to spreading posts that violate group policies. The categorization post shows increased performance where there are large numbers of members in a social group by performing linguistic clustering. The contribution work is to implement location-aware personalized posts recommendation using users’ behavioral patterns and their geographic location. Another, important work is to implement text-to-speech system converting English text into speech using speech synthesis technique. The system gives rating to the users who shares posts depending on clustering. Also system provides read later functionality to the user side. The system has been tested on Twitter API group data where a significant solution to an unaddressed problem associated with social networking groups is offered.