  Many word clouds provide no semantics to the word placement, but use a random
layout optimized solely for aesthetic purposes. We propose a novel approach to
model word significance and word affinity within a document, and in comparison
to a large background corpus. We demonstrate its usefulness for generating more
meaningful word clouds as a visual summary of a given document. We then select
keywords based on their significance and construct the word cloud based on the
derived affinity. Based on a modified t-distributed stochastic neighbor
embedding (t-SNE), we generate a semantic word placement. For words that
cooccur significantly, we include edges, and cluster the words according to
their cooccurrence. For this we designed a scalable and memory-efficient
sketch-based approach usable on commodity hardware to aggregate the required
corpus statistics needed for normalization, and for identifying keywords as
well as significant cooccurences. We empirically validate our approch using a
large Wikipedia corpus.
