Semantic Word Clouds with Background Corpus Normalization and t-distributed Stochastic Neighbor Embedding Article Swipe
YOU?
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· 2017
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.1708.03569
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.
Related Topics To Compare & Contrast
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1708.03569
- https://arxiv.org/pdf/1708.03569
- OA Status
- green
- Cited By
- 16
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2745765137