Can Social Ontological Knowledge Representations be Measured Using Machine Learning?

Personal Social Ontology (PSO), it is proposed, is how an individual perceives the ontological properties of terms. For example, an absolute fatalist would arguably use terms that remove any form of agency from a person. Such fatalism has the impact of ontologically defining acts such as winning, victory and success, for example, in a manner that is contrary to how a non-fatalist would ontologically define them. While both a fatalist and non-fatalist would agree on the dictionary definition of these terms, they would differ on what and how they can be caused. This difference between the two individuals, it is argued, can be induced from the co-occurrence of terms used by each individual. That such co-occurrence carries an implied social ontology, one that is specific to that person. The use of principal social perceptions -as evidenced by the social psychology and social neuroscience literature, is put forward as a viable method to feature engineer such texts. With the natural language characterisation of these features, they are then usable in machine learning pipelines.