Static Text-Based Data Visualizations: An Overview and a Sampler

Static Text-Based Data Visualizations: An Overview and a Sampler

Shalin Hai-Jew (Kansas State University, USA)
DOI: 10.4018/978-1-5225-1837-2.ch032
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Abstract

Data visualizations have enhanced human understandings of various types of quantitative data for many years. Of late, text-based data visualizations have been used informally and formally on the WWW and Internet as well as for research. This chapter describes this phenomenon of text-based data visualizations by describing how many of the most common ones are created, where the underlying textual datasets are extracted from, how text-based data visualizations are analyzed, and the limits of such graphical depictions. While this work does not provide a comprehensive view of static (non-dynamic) text-based data visualizations, many of the most common ones are introduced. These visualizations are created using a variety of common commercial and open-source tools including Microsoft Excel, Google Books Ngram Viewer, Microsoft Visio, NVivo 10, Maltego Tungsten, CASOS AutoMap and ORA NetScenes, FreeMind, Wordle, UCINET and NetDraw, and Tableau Public. It is assumed that readers have a basic knowledge of machine-based text analysis.
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A Review Of The Literature

Language is a core element of culture, and words carry critical concepts. Language is a central element of human socializing and inter-communications. It is the core vehicle for the dissemination and communication of research and of popular understandings of the world. Language is polysemic or many-meaninged; based on its interpretation, it may convey a range of ideas and impressions. Messaging may be understood whether the initial communicator consciously or unconsciously intended to share particular information; meaning is not necessarily dependent on the conscious intentions of those wielding the language. The centrality of language in the human experience in part explains the power of machine-based text analysis. As a data source, language is often rendered into textual form for analysis (whether the original data was a video, audio, image, or other multimedia type).

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