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Using the 6,638 case descriptions of societal impact submitted for evaluationin the Research Excellence Framework (REF 2014), we replicate the topic model(Latent Dirichlet Allocation or LDA) made in this context and compare theresults with factor-analytic results using a traditional word-document matrix(Principal Component Analysis or PCA). Removing a small fraction of documentsfrom the sample, for example, has on average a much larger impact on LDA thanon PCA-based models to the extent that the largest distortion in the case ofPCA has less effect than the smallest distortion of LDA-based models. In termsof semantic coherence, however, LDA models outperform PCA-based models. Thetopic models inform us about the statistical properties of the document setsunder study, but the results are statistical and should not be used for asemantic interpretation - for example, in grant selections and micro-decisionmaking, or scholarly work-without follow-up using domain-specific semanticmaps.

Topic Modelling of Empirical Text Corpora: Validity, Reliability, and Reproducibility in Comparison to Semantic Maps