Bibliometric Mapping of Computer and Information Ethics (Richard Heersmink) Overviews can be found in handbooks (Floridi2010;Johnson2008; Tavani and Himma2008; van den Hovenand Weckert2008), encyclopedia articles (Brey and Sør-aker2009; Bynum2008), and papers (Buchanan1999).These sources give overviews of the C&IE field by pro-viding a historical sketch of the field and by discussing andanalyzing the most important topics.In this paper, we also aim to provide an overview of theC&IE field.
However, the approach that we take is differentfrom previous approaches. In contrast to earlier work, wetake a bibliometric mapping approach. This approach isdata driven and relies heavily on computer algorithms andvisualization techniques. The approach can be seen ascomplementary to the earlier studies mentioned above. Lexico Web Page (downloadable app for the PC) TextArc.org Home. Playing with Text « Gilad Lotan. I’ve recently been inspired by some text visualization methods.
Of specific interest to me are Edward Tufte’s Sparklines, simple word sized graphics, which allow their users to get the gist of what’s happening with the data without needing to fully read it all. The graphic below is a 13th century manuscript bible with these amazing decorative tendrils. Reminds me of most standard Hebrew bibles which has scribbles signifying musical phrases and tones drawn above and below the words. I wonder why we’ve lost these decorations and symbols from our text styling. This is slowly coming back with (::cough::) emoticons. Another project, Similar Diversity, is a well-done textual analysis which visualizes words from English translations of Holy scriptures. Different colors signify the holy book the word appeared in, while size, obviously, relates to the number of times the word came up.
This is an illustration of the relationships between the words. Text Visualization - cs448b - Data Visualization. Information Visualization for Text Analysis (Ch 11) From the book Search User Interfaces, published by Cambridge University Press.
Copyright © 2009 by Marti A. Hearst. As discussed in the previous chapter, visualization when applied to text seems to be most effective for specialists doing data analysis. Although this is an exciting field, it is not what most people think of when one talks about search interfaces. Unfortunately, some researchers working on visualization of text conflate search tasks with data analysis tasks. Figure 11.1: The TAKMI text mining interface. This chapter describes ideas that have been put forward for understanding the contents of text collections from a more analytical point of view. 11.1: Visualization for Text Mining There is great interest in the field of text mining, which can be defined as the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources (Hearst, 1999a, Feldman and Sanger, 2006). Philip Leifeld - Discourse Network Analyzer - Screenshots - Screenshots and overview.
Visualization Strategies: Text & Documents » Tim Showers – Web Development, Design, and Data Visualization» Blog Archive. Whether it’s a campaign speech by a presidential contender, or a 300 page bestselling novel, large bodies of text are among the most requested topics for condensing into an infographic.
The purpose can vary from highlighting specific relations to contrasting points or use of language, but all of the following methods focus on distilling a volume of text down to a visualization. Volumetric Comparisons: Tag Clouds & Wordles: A Tag Cloud from Many Eyes Among the most common visualizations is the so called ‘Tag Cloud‘, which is just a list of words or descriptions that are sized by some relevance measure, usually number of repetitions in the data set.
Tag clouds are often used to highlight and compare themes within the document, such as stripping down a U.S. A Sample Wordle from Closely related to tag clouds are wordles, which are more artistically arranged (and often vibrantly colored) versions of a text. Word Spectrum Diagrams World Spectrum Diagram from the Google Data Set. A Text Visualization Tool. By: Jeff Clark Date: Thu, 22 Mar 2007 The idea of an interactive tool to explore the structure of a text document has always intrigued me.
Visually highlighting key terms from a document and the relationships between them might be an effective way to gain new insights. I have been playing around for a while creating such a tool and have decided it's interesting enough to show here. There are quite a few things I don't like about it but I'm going to set it aside for a bit. I don't like to embed java applications directly in my feed so the real application can be found farther down this post - the part that you have to read directly on my site.
(image only - the interactive application is farther down) The top left set of connected circles represents a partial view of a graph showing inter-relationships between words. The top right shows a collection of bar graphs giving the distribution of the primary words across the entire document. Give it a try !