An R package turns psychological scale items into vectors for factor analysis. semanticfa, published on arXiv, uses a language model to convert each item into an embedding, then performs factor analysis on the semantic similarities between items. It can also interpret factors, flag duplicate items, create short forms, and screen new items.
The example uses the 50-item IPIP Big Five personality scale and the Qwen3-Embedding-8B embedding model. Compared with factor analysis of responses from more than 870,000 real respondents, the number of factors retained fell between five and six; applying the selection rule yielded five. Three factors mainly corresponded to a single dimension, while the other two mixed most of the extraversion items with five agreeableness items.
The consistency of each factor with its closest-matching human-derived factor ranged from 0.92 for openness to 0.52 for agreeableness. The authors stress that this is a supplement, not a replacement for factor analysis based on responses. This is a preprint that has not undergone peer review; conclusions are limited to the scope of the experiments described in the abstract.
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The example uses the 50-item IPIP Big Five personality scale and the Qwen3-Embedding-8B embedding model. Compared with factor analysis of responses from more than 870,000 real respondents, the number of factors retained fell between five and six; applying the selection rule yielded five. Three factors mainly corresponded to a single dimension, while the other two mixed most of the extraversion items with five agreeableness items.
The consistency of each factor with its closest-matching human-derived factor ranged from 0.92 for openness to 0.52 for agreeableness. The authors stress that this is a supplement, not a replacement for factor analysis based on responses. This is a preprint that has not undergone peer review; conclusions are limited to the scope of the experiments described in the abstract.
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