Skip to main content
Jump to: navigation, search

GEF/GEF4/Cloudio/User Guide

< GEF‎ | GEF4‎ | Cloudio
Revision as of 08:26, 28 January 2016 by (Talk | contribs)

Note to non-wiki readers: This documentation is generated from the Eclipse wiki - if you have corrections or additions it would be awesome if you added them in the original wiki page.

The GEF4 Cloudio component provides a Tag Cloud view that can be used to create word clouds.


The words, which are used to render the word cloud can be loaded from a text file via the 'File -> Load File'. In case the file contains words that should be excluded from the word cloud, a list of stop words (blacklist) can be loaded in addition via 'File -> Load Stopwords'. After having loaded words and/or stop words, the tag cloud is automatically rendered. The rendered tag cloud can be exported to an PNG file via 'File -> Export Image'.

The view delivers various controls to adjust the generation of the word cloud, including background mask (a square PNG image containing black and white pixels only, where black pixels are interpreted as used, such that strings will be drawn on white areas only), font min and max sizes, boost (count) and boost factor, angles, scales, and axis variation. Furthermore, colors and fonts can be specified. After changes to these properties, the tag cloud can be rendered again using 'Re-Position' or 'Re-Layout'.

Below are some examples of tag clouds generated with Cloudio. The images were created with the help of the TagCloud view, modifying different parameters (such as colors, fonts or rotation angles).


Zest cloudio woyzeck.png

Created from Georg Büchner's Woyzeck. The most frequent word was boosted.


Zest cloudio winnetou.png

Karl May's Winnetou III, using two different fonts, 45-degree rotation and a relatively large x-axis variation when placing the words.


Zest cloudio nietzsche.png

'Also sprach Zarathustra', by Nietzsche. 90 degree rotation and a large x-axis variation.

Woyzeck Cluster

Zest cloudio woyzeck cluster.png

Same text as in the first example, but with a modified layout algorithm and label provider: Both labels and initial position are assigned based on the first character of the word (for instance, words starting with a to l are at the bottom left). Doesn't really look good, but shows how to extend the functionality to realize a cluster visualization or else...

Back to the top