In this paper, we investigate the impact of emotions on author profiling, concretely
identifying age and gender. Firstly, we propose the EmoGraph method for modelling the
way people use the language to express themselves on the basis of an emotion-labelled
graph. We apply this representation model for identifying gender and age in the Spanish
partition of the PAN-AP-13 corpus, obtaining comparable results to the best performing
systems of the PAN Lab of CLEF.