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Psychological Constructs in Shared Semantic Space

arXiv 2605.26801 ↗First posted 26 May 2026cs.CLcited by 0

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Abstract (arXiv)

Psychological constructs are often measured in separate instruments, datasets, and research traditions, which makes direct comparison difficult. This paper proposes a framework for making such constructs semantically commensurate by representing and comparing them as directions in a shared word-embedding space. Using Supervised Semantic Differential, we estimate construct-specific semantic gradients from text-outcome associations and project them onto theoretically motivated reference axes. As an initial test case, we use Valence, Arousal, and Dominance (VAD) as an affective coordinate system. First, we recover interpretable VAD directions from English word-level affective norms. Second, we project semantic gradients for 27 GoEmotions categories into this space and recover the expected organization of emotions, especially along valence and arousal. Third, we apply the same procedure to Big Five personality domains and facets derived from IPIP-NEO-300 item-factor associations. Domain-level placements are broadly coherent, while facet-level results are more exploratory because they rely on sparse questionnaire text. The results suggest that embedding spaces can support construct-level comparison across otherwise incommensurable psychological measurements, provided that semantic placements are assessed for stability and interpretability.

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References · 7

  1. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  2. 2020GoEmotions: A Dataset of Fine-Grained Emotionscited by 479
  3. 2017Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypescited by 251
  4. 2016Diachronic Word Embeddings Reveal Statistical Laws of Semantic Changecited by 219
  5. 2017All-but-the-Top: Simple and Effective Postprocessing for Word Representationscited by 205
  6. 2018The Geometry of Culture: Analyzing Meaning through Word Embeddingscited by 87
  7. 2026The Prediction-Measurement Gap: Toward Meaning Representations as Scientific Instrumentscited by 1

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