Human emotions and their modelling are increasingly understood to be a crucial aspect in the development of intelligent systems. Over the past years, in fact, the adoption of psychological models of emotions has become a common trend among researchers and engineers working in the sphere of affective computing. Because of the elusive nature of emotions and the ambiguity of natural language, however, psychologists have developed many different affect models, which often are not suitable for the design of applications in fields such as affective HCI, social data mining, and sentiment analysis. To this end, we propose a novel biologically-inspired and psychologically-motivated emotion categorisation model that goes beyond mere categorical and dimensional approaches. Such model represents affective states both through labels and through four independent but concomitant affective dimensions, which can potentially describe the full range of emotional experiences that are rooted in any of us.