An Interests Discovery Approach in Social Networks Based on a Semantically Enriched Bayesian Network Model
Abstract
Knowing the interests of users in Social Networking Systems becomes essential for User Modeling. Interests discovery from users posts based on standard text classification techniques such as the Bag Of Words fails to catch the implicit relations between terms. We propose an approach that automatically generates an ordered list of candidate topics of interests given the text of the users posts. The approach generate terms and segments, enriches them semantically from world knowledge, and creates a Bayesian Network to model the syntactic and semantic relations. After that it uses probabilistic inference to elect the list of candidate topics of interests which have the highest posterior probability given the explicit and implicit features in users posts as observed evidences. A primitive evaluation has been conducted using manually annotated data set consisting of 40 Twitter users. The results showed that our approach outperforms the Bag Of Words technique, and that it has promising indications for effectively detecting interests of users in Social Networking Systems.