An Interests Discovery Approach in Social Networks Based on Semantically Enriched Graphs

Studying the text messages of a user such as his posts in Facebook or his tweets in Twitter can help in detecting his topics of interests. User in Social Network Systems (SNS) posts text messages about a wide diverse of topics. Posts usually written in a non-standard language, which make it not applicable to the standard Natural Language Processing (NLP) techniques used to catch the relations between words in text. In many cases there are semantic relations between the contained entities of posts that can infer the interest of the user. Bag-Of-Words (BOW) based text classification techniques classify this kind of messages to a wide diverse of topics, but they fail in catching the implicit semantic relation between the contained entities. In this paper we propose a technique to discover the implicit semantic relations between entities in text messages, which can infer the interests of a user. The proposed technique based on a semantically enriched graph representation of entities contained in text messages generated by a user, a new algorithm (Root-Path-Degree) is invented and used to find the most representative sub-graph that reflects the semantic implicit interests of the user. An evaluation was done using manually annotated posts of 687 Facebook users. Precision and Recall results showed our technique performs better than the standard BOW technique.