ThemeCrowds: multiresolution summaries of twitter usage
Proceedings of the 3rd international workshop on Search and mining user-generated contents
TopicNets: Visual Analysis of Large Text Corpora with Topic Modeling
ACM Transactions on Intelligent Systems and Technology (TIST)
Augmented visualization with natural feature tracking
Proceedings of the 10th International Conference on Mobile and Ubiquitous Multimedia
Interpretation and trust: designing model-driven visualizations for text analysis
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
“Without the clutter of unimportant words”: Descriptive keyphrases for text visualization
ACM Transactions on Computer-Human Interaction (TOCHI)
Seeing beyond reading: a survey on visual text analytics
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Exploring collections of tagged text for literary scholarship
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Semantic-preservingword clouds by seam carving
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Multifaceted visual analytics for healthcare applications
IBM Journal of Research and Development
FacetClouds: exploring tag clouds for multi-dimensional data
Proceedings of Graphics Interface 2013
Visual analysis of set relations in a graph
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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Documents in rich text corpora usually contain multiple facets of information. For example, an article about a specific disease often consists of different facets such as symptom, treatment, cause, diagnosis, prognosis, and prevention. Thus, documents may have different relations based on different facets. Powerful search tools have been developed to help users locate lists of individual documents that are most related to specific keywords. However, there is a lack of effective analysis tools that reveal the multifaceted relations of documents within or cross the document clusters. In this paper, we present FacetAtlas, a multifaceted visualization technique for visually analyzing rich text corpora. FacetAtlas combines search technology with advanced visual analytical tools to convey both global and local patterns simultaneously. We describe several unique aspects of FacetAtlas, including (1) node cliques and multifaceted edges, (2) an optimized density map, and (3) automated opacity pattern enhancement for highlighting visual patterns, (4) interactive context switch between facets. In addition, we demonstrate the power of FacetAtlas through a case study that targets patient education in the health care domain. Our evaluation shows the benefits of this work, especially in support of complex multifaceted data analysis.