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SoS TextVis: An Extended Survey of Surveys on Text Visualization

Department of Copmuter Science, Swansea University, Swansea SA1 8EN, UK
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Computers 2019, 8(1), 17; https://doi.org/10.3390/computers8010017
Received: 14 January 2019 / Revised: 8 February 2019 / Accepted: 18 February 2019 / Published: 20 February 2019
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Abstract

Text visualization is a rapidly growing sub-field of information visualization and visual analytics. There are many approaches and techniques introduced every year to address a wide range of challenges and analysis tasks, enabling researchers from different disciplines to obtain leading-edge knowledge from digitized collections of text. This can be challenging particularly when the data is massive. Additionally, the sources of digital text have spread substantially in the last decades in various forms, such as web pages, blogs, twitter, email, electronic publications, and digitized books. In response to the explosion of text visualization research literature, the first text visualization survey article was published in 2010. Furthermore, there are a growing number of surveys that review existing techniques and classify them based on text research methodology. In this work, we aim to present the first Survey of Surveys (SoS) that review all of the surveys and state-of-the-art papers on text visualization techniques and provide an SoS classification. We study and compare the 14 surveys, and categorize them into five groups: (1) Document-centered, (2) user task analysis, (3) cross-disciplinary, (4) multi-faceted, and (5) satellite-themed. We provide survey recommendations for researchers in the field of text visualization. The result is a very unique, valuable starting point and overview of the current state-of-the-art in text visualization research literature. View Full-Text
Keywords: Survey of Surveys; text visualization; information visualization Survey of Surveys; text visualization; information visualization
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Alharbi, M.; Laramee, R.S. SoS TextVis: An Extended Survey of Surveys on Text Visualization. Computers 2019, 8, 17.

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