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Description: We assess whether the classic psychometric paradigm of risk perception can be improved or supplanted by novel approaches relying on language embeddings. To this end, we introduce the Basel Risk Norms, a large data set covering 1,004 distinct sources of risk (e.g., vaccination, nuclear energy, artificial intelligence) and compare the psychometric paradigm against novel text and free-association embeddings in predicting risk perception. We find that an ensemble model combining text and free association rivals the predictive accuracy of the psychomet- ric paradigm, captures additional aspects not accounted for by the classic approach, and has greater range of applicability to real-world text data, such as news headlines. Overall, our results establish the ensemble of text and free-association embeddings as a promising new tool for researchers and policymakers to track real-world risk perception.