The Supervised Semantic Differential (SSD) is a method that allows researchers to statistically assess whether people systematically differ in the ways they write or speak about specific concepts, depending on their psychological predispositions. It also provides mixed-method cues about the nature of these meaning differences and how they map onto those predispositions. Briefly, SSD is a linguistic-computational approach that models individual differences in meaning by constructing personal concept vectors from the word-embedding contexts of key concepts in participants' texts, then relating these vectors to psychological variables using classical linear regression in a reduced semantic space. The resulting semantic direction is interpreted through qualitative inspection and clustering of nearest neighbours in the embedding space, yielding psychologically meaningful contrasts in how concepts are understood. Owing to its versatility and sensitivity in small samples, SSD has been applied across a wide range of domains in roughly six months since its development. Applications include shifts in how people conceptualize their own country as a function of collective narcissism; the self, as a function of personality traits; and how they portray travel, discuss abortion, or describe AI in relation to narcissistic dispositions. SSD was also used to examine self-concept differences across clinical populations varying in personality pathology (borderline and narcissistic personality disorder relative to controls) and meaning shifts in ideologically loaded concepts such as climate change in relation to willingness to act on it. In the talk, I will explain the core logic of this procedure, present findings from studies where I have already applied it and show how SSD can enrich the methodological toolkit for studying meaning, language, and individual differences in the social sciences.