Interpretable Semantic Gradients in SSD: A PCA Sweep Approach and a Case Study on AI Discourse

ArXi:2603.13038v1 Announce Type: new Supervised Semantic Differential (SSD) is a mixed quantitative-interpretive method that models how text meaning varies with continuous individual-difference variables by estimating a semantic gradient in an embedding space and interpreting its poles through clustering and text retrieval. SSD applies PCA before regression, but currently no systematic method exists for choosing the number of retained components,