Mapping the Terrain of Political Language with LLMs

PCA visualization of 56 political word pairs encoded with all-MiniLM-L6-v2 (384-dim → 2-dim). Red/blue markers denote opposing semantic poles. PC1 (5.7%) captures ideological orientation; PC2 (4.3%) separates affective vs. structural terms. Low cumulative variance (10%) indicates high-dimensional semantic structure—most political language relationships exist beyond these two principal components.

This PCA visualization reveals how political concepts naturally cluster in semantic space. Red and blue markers aren't arbitrary—they trace the dimensional boundaries where ideology meets emotion, where "faith" neighbors "trust" and "despair" sits near "fragile."
Notice how "optimism" and "pessimism" anchor opposite poles, while terms like "altruism," "secularism," and "ideology" form their own constellation. The clustering suggests these aren't just words—they're cognitive landmarks in how we structure political thought.
The low variance (5.7% and 4.3%) reminds us: political language is high-dimensional. What we see here is just the shadow of something far more complex.

NEXT:  We will use these words to define new dimensions for other visualizations.  Stay tuned!