About those downloads...

If you use TreeCloud, please cite treecloud.org or:
Philippe Gambette, Jean Véronis: Visualising a Text with a Tree Cloud, In Locarek-Junge H. and Weihs C., editors, Classification as a Tool of Research, Proc. of IFCS'09 (11th Conference of the International Federation of Classification Societies) (supplementary material).

For feedback, questions, feature requests or bug reports for TreeCloud, etc., please contact me, or leave a message on Jean Véronis's blog or mine.

Downloads

• TreeCloud 1.3 1.4.2β (13/12/2009 15/04/2014) for Windows, Linux, Mac: You can download the archive Treecloud.zip the archive Treecloud1.4.2beta.zip (to use it, you need Python2.X, Java, and SplitsTree 4.10 on your system) and decompress it in any folder. It contains the following files:
  • Treecloud.exe, a graphical user interface for Windows.
  • Treecloud.py, the main Python script.
  • TreecloudFunctions.py, a Python library of functions used by Treecloud.py.
  • the TreeCloud manual, to know how to install and use the program
  • English, French (adapted from the Dico stoplist) and German stoplists, you can find other stoplists here.
  • HISTORY.txt: changelog
  • COPYING.txt: GPL License
• to visualize the location in the text of the words which appear in the tree cloud, you can use AntConc (especially the "concordance plot" tab).
• Nuage arboré, an optimized C program and web interface by Jean-Charles Bontemps, which is used as the basis of the tree cloud building interface on this website.
• PhyloPlot, a JavaScript library written by Yu Zheng, which uses D3.js to draw a tree cloud, given the Newick code of the word tree as well as the colors and sizes of the words (see a demo here).
• Outdated TreeCloud 0.6 for Windows (Delphi 6 sources, in particular UPGMA and EqualAngle in the Unit1.pas file, in the functions UPGMA, sortLeaves, computeDrawing and draw, changelog).

Next versions

I'm seriously considering the following ideas for the next version of TreeCloud, they are easy to implement:

• output a regular tag cloud instead of just providing an input file for TagCloud Builder,
• multiple concordance: provide a set of words as parameters, TreeCloud retrieves their context (x words before, x words after) in the text and outputs this file, and also uses it as input to build a tree cloud focused on this set of words (David Barrowcliff gave this idea),
• list of word changes: define sets of words which can be replaced by another one, for example "love" "passion" "feeling" are replaced by "love" in a preprocessing step (Delphine and other users gave this idea),

I will solve the following problems when I find a solution:

• avoid using SplitsTree to compute the tree, and use other tree reconstruction algorithm,
• implement the tree reconstruction algorithm by Barthélémy and Luong,
• avoid using SplitsTree to visualize the tree (maybe Scriptree),
• compute cooccurrence according to the hypergeometric model (needs approximations to compute binomials).