Edinburgh Napier University

  In human cognition, the capacity to reason and make decisions is strictly dependent on our common sense knowledge about the world and our inner emotional states: we call this ability affective common sense reasoning. In previous works, graph mining and multi-dimensionality reduction techniques have been employed in attempt to emulate such a process and, hence, to semantically and affectively analyze natural language text. In this work, we exploit a novel cognitive model based on the combined use of principal component analysis and artificial neural networks to perform reasoning on a knowledge base obtained by merging a graph representation of common sense with a linguistic resource for the lexical representation of affect. Results show a noticeable improvement in emotion recognition from natural language text and pave the way for more bio-inspired approaches to the emulation of affective common sense reasoning.