  Word clouds became a standard tool for presenting results of natural language
processing methods such as topic modelling. They exhibit most important words,
where word size is often chosen proportional to the relevance of words within a
topic. In the latent Dirichlet allocation (LDA) model, word clouds are
graphical presentations of a vector of weights for words within a topic. These
vectors are the result of a statistical procedure based on a specific corpus.
Therefore, they are subject to uncertainty coming from different sources as
sample selection, random components in the optimization algorithm, or parameter
settings. A novel approach for presenting word clouds including information on
such types of uncertainty is introduced and illustrated with an application of
the LDA model to conference abstracts.
