Automatic text processing
A self-organizing semantic map for information retrieval
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
Bead: explorations in information visualization
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
The visual display of information in an information retrieval environment
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
TOPIC ISLANDS—a wavelet-based text visualization system
Proceedings of the conference on Visualization '98
The STARLIGHT information visualization system
Readings in information visualization
Semantic Road Maps for Literature Searchers
Journal of the ACM (JACM)
Computing in Science and Engineering
The Shape of Shakespeare: Visualizing Text using Implicit Surfaces
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
Visualizing the non-visual: spatial analysis and interaction with information from text documents
INFOVIS '95 Proceedings of the 1995 IEEE Symposium on Information Visualization
Semiology of graphics
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Visualization of text streams: a survey
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
Exploring and analyzing documents with OLAP
Proceedings of the 5th Ph.D. workshop on Information and knowledge
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The termvisual text analyticsdescribes a class of information analysis techniques and processes that enable knowledge discovery via the use of interactive graphical representations of textual data. These techniques enable discovery and understanding via the recruitment of human visual pattern recognition and spatial reasoning capabilities. Visual text analytics is a subclass of visual data mining / visual analytics, which more generally encompasses analytical techniques that employ visualization of non-physically-based (or "abstract") data of all types. Text visualizationis a key component in visual text analytics. While the term "text visualization" has been used to describe a variety of methods for visualizing both structured and unstructured characteristics of text-based data, it is most closely associated with techniques for depicting the semantic characteristics of the free-text components of documents in large document collections. In contrast with text clustering techniques which serve only to partition text corpora into sets of related items, these so-called semantic mappingmethods also typically strive to depict detailed inter- and intra-set similarity structure. Text analytics software typically couples semantic mapping techniques with additional visualization techniques to enable interactive comparison of semantic structure with other characteristics of the information, such as publication date or citation information. In this way, value can be derived from the material in the form of multidimensional relationship patterns existing among the discrete items in the collection. The ultimate goal of these techniques is to enable human understanding and reasoning about the contents of large and complexly related text collections.