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Social activity streams provide information both about the user’s interests and about the way in which they engage with real world entities. Recent research has provided evidence of the presence of emergent semantics in such streams. In this work, we explore whether the online discourse of user’s social activities can convey meaningful contextual information. We introduce a usercentric methodology based on tensor analysis for deriving personal vocabularies given an entity-based context. By extracting entities (eg location, organisation, people) from the user’s stream content, we explore the data structures that emerge from the user’s interrelationship with these entities. Our experimental results revealed that the simultaneous correlation of entities leads to the identification of concepts which are relevant to the user given a specific context. This methodology is relevant for mobile application designers (1) in fostering user entity-based ontologies for merging user context in pervasive environments,(2) for personalising entity-based recommendations.