{"id":174,"date":"2008-10-13T08:36:36","date_gmt":"2008-10-13T13:36:36","guid":{"rendered":"http:\/\/www.kralidis.ca\/blog\/?p=174"},"modified":"2008-10-13T08:59:00","modified_gmt":"2008-10-13T13:59:00","slug":"clear-skies-with-python-and-tag-clouds","status":"publish","type":"post","link":"https:\/\/www.kralidis.ca\/blog\/2008\/10\/13\/clear-skies-with-python-and-tag-clouds\/","title":{"rendered":"Clear Skies with Python and Tag Clouds"},"content":{"rendered":"

I’ve been researching tag clouds<\/a> in the last few days. I think tag clouds can help geospatial search front ends in giving the user a “weighted list”, to get them to what they want quickly and more efficiently.<\/p>\n

\"tag<\/a>

tag cloud example<\/p><\/div>\n

The following Python script takes a list of terms as input.\u00a0 Such a list can be derived from many things, such as an existing taxonomy, analyzing an httpd log file for commonly used search terms, user votes, and so on.\u00a0 In this (simple) example, we use comma separated input.<\/p>\n

By creating a term and count dictionary, this sets up the anatomy of a tag cloud.\u00a0 From here, you can pass this for output to the web (i.e. font sizes, colours, etc.).\u00a0 Here we output this to an APML<\/a> document, which is often used to represent tag clouds<\/a>.\u00a0 You can then use tools such as cluztr<\/a> to generate tag clouds with ease.<\/p>\n

Considerations:<\/p>\n