The phenomenon of economic uncertainty has attracted considerable attention in recent years. New indicators have been introduced aiming at measuring uncertainty and its potential economic consequences. Still, the Corona pandemic has hit the world economy virtually out of the blue. In this paper, we argue that, while it is clear that true uncertainty, by definition, cannot be forecasted, better early warning systems could be built. To further this goal, we propose a new taxonomy of economic uncertainty and construct a news-based indicator that captures different kinds of uncertainty, some of which may precede others. If we are able to detect the preludes of an uncertainty shock, we may be able to gauge its size and potential economic impact early on. In earlier writings (Müller et al. 2018, Müller 2020a) we demonstrated the feasibility of Latent Dirichlet Allocation (LDA) for gauging uncertainty. Here, we base our analysis on an enhanced data set, a broader query, and we propose a routine to scan the recent past for new sources of uncertainty. Based on a text corpus of more than 750.000 newspaper articles published since 2008, we run several topic models of the LDA type. As an unsupervised text mining technique LDA has the potential to make economic indicators more sensitive to hitherto unknown – or overlooked – factors of economically relevant uncertainty. Our results are preliminary, yet encouraging. The notion that economic uncertainty comes in three types, two of which, market-based and economic policy uncertainty, may reinforce one another, while the third type is truly exogeneous, is broadly supported by our empirical approach. The Uncertainty Perception Indicator (UPI) is able to shed light on the links between the three categories of uncertainty and is systematically open to new developments; it is designed to detect not merely known unknowns (e.g. fiscal and monetary policy, trade policy, regulation), but also surprising unknowns (e.g. technological, ecological, social changes).