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Many word clouds provide no semantics to the word placement, but use a randomlayout optimized solely for aesthetic purposes. We propose a novel approach tomodel word significance and word affinity within a document, and in comparisonto a large background corpus. We demonstrate its usefulness for generating moremeaningful word clouds as a visual summary of a given document. We then selectkeywords based on their significance and construct the word cloud based on thederived affinity. Based on a modified t-distributed stochastic neighborembedding (t-SNE), we generate a semantic word placement. For words thatcooccur significantly, we include edges, and cluster the words according totheir cooccurrence. For this we designed a scalable and memory-efficientsketch-based approach usable on commodity hardware to aggregate the requiredcorpus statistics needed for normalization, and for identifying keywords aswell as significant cooccurences. We empirically validate our approch using alarge Wikipedia corpus.

Semantic Word Clouds with Background Corpus Normalization and t-distributed Stochastic Neighbor Embedding