Hidden interests of an individual can be inferred by keenly observing their social profile data and blending this data with a semantic network. Getting user interests without user’s manual intervention is very beneficial for companies feeding on user’s regular behavior. This paper provides the entire idea of how to retrieve the user’s hidden interests and what is a semantic network. Twitter is the preferred social platform for entities extraction. We started basically by gathering entities like hashtags and keywords from the tweets posted by an individual. And simultaneously created a Semantic Network using Wikipedia’s taxonomy of categories and subcategories and pages implementing a concept called Labeled Property Graph (LPG). Matching the pre-obtained tweet entities with the Wikipedia graph of Categories and Pages a graph is generated called Hierarchical Interest Graph (HIG) which contains so called hidden interests of user. HIG of an individual is an isolated entity and may never match with others’ HIG.