Basically, the larger the word the more frequently it appears in the text. Stop words were discarded. I also adjusted the size based on the relative frequency of the word in the canada dataset versus a baseline dataset containing tweets about india and china. A word like 'country' or 'travel' is used approximately the same for canada as for india and china and so will be de-emphasized. Words like 'hockey' , 'canadian', 'snow' and place names within canada will appear bigger. Because of the baseline content the result will not properly reflect any strong associations between canada and india or canada and china. As usual you can click on a word to see the current twitter search results.

Feel free to print this in any newspaper or magazine. I only ask that you keep the reference to http://neoformix.com and that you send me an email letting me know.

Click on the image to download a hi-res PDF version suitable for printing

Feel free to print this in any newspaper or magazine. I only ask that you keep the reference to http://neoformix.com and that you send me an email letting me know.

Click on the image to download a hi-res PDF version suitable for printing

A couple of points of interest:

• Celebrity interest ranked by number of tweets is Michael > Farrah > Ed with ratios 62:6:1
• Ed was mentioned together with both Michael and Farrah more often than he was by himself

To explore the data using the interactive application click on the image below or this link: Twitter Venn for #michaeljackson, #farrahfawcett, and #edmcmahon.

The image below was created from the cluster members data, the similarity between clusters, and the similarity within each cluster. To minimize line clutter I am only drawing a connection if it is one of the top 2 strongest for either end node. The clustering and layout code is based on what I used for the Toronto Twitter Community project but has been recently enhanced to support some new client work.

Here is the PDF version of the Twitter Employee Clusters.

Much more enjoyable was the hour or so we spent doing a virtual tour of Malta using Google Earth. My father-in-law was born there and we had great fun zooming in with the aerial views finding the house where he lived, the church where he was baptized, etc. We were also able to easily see wonderful pictures of the many famous churches and natural features like the Blue Grotto. It's a beautiful and fascinating place and I'd love to visit sometime.

Well, the ideas of Malta, word search puzzles, and the usual mishmash from my coding projects mixed together in my brain while I was sleeping and I woke up early realizing I could easily write a tool to create 'Shaped Word Search Puzzles'. Basically, I can take a template image and a list of words and automatically construct a word search puzzle shaped and coloured to match the image.

The first example is below and uses a Maltese Cross with a list of words related to Malta. Most of the words are place names but there are a few other things mixed in as well. For example, Pastizzi are one of my favourite Maltese foods.

Click on the image to download a hi-res PDF version suitable for printing

Static image of the phrase net for #IranElection Tweet Data (see below for interactive version)

See below for the interactive version.

Feel free to follow JeffClark on Twitter to get more updates on my work.

I'm now using the standard Twitter API to retrieve the tweets for any simple user query and it will graph up to a maximum of 1000 tweets irregardless of how far back they go. The difference is shown below for Clay Shirky. The second image shows the new improved results which, for him, go back almost a year. The graph is much richer than the first one which can only base the graph on tweets in the last two weeks.

Previous results limited to approximately 14 days due to Twitter Search limitation
 
New results for simple queries of the type from:twitterid

Others in this series: FlowerTank, FlowerCycle, and John Lennon Flower Portrait.

Click on the image to see the current Twitter venn diagram for these three terms.

The central people in this set appear to be RyanSeacrest, PaulaAbdul, and TheEllenShow. The similarity score between Ryan and Paula is 19.8% and the top words connecting them together are: 'radio', 'game', 'guys', 'adam', 'movie', 'coast', 'studio', and their respective Twitter IDs.

Another interesting grouping is BarackObama, schwarzenegger, and timoreilly. The similarity score between Obama and Schwarzenegger is 16.7% with the top connecting words being 'health' , 'care', 'video', 'president', 'address', 'vote', and 'event'.

I included jtimberlake in the analysis as well but he was removed from the final graph because he wasn't connected strongly enough with anybody else. His closest match was only 4.5% and was with Oprah.

Here are links to Word Portaits of Obama and Einstein.

This first view shows that there was more discussion of 'peace' than 'war' and that words like 'palestinian', 'israel', and 'god' were highly associated with 'peace' relative to the other highlighted words.

Click image to see a larger version

This second view below is of the same graph but with different words pegged in place. The terms 'nuclear' 'weapons' and 'united' 'states' are both closer to 'iran' than the other countries. Similarly, 'women' 'denied' 'equal' is more associated with 'afghanistan'.

Click image to see a larger version

An obvious way to improve these would be to use word stemming to combine different forms of the same word. For example, 'muslim' and 'muslims' would use one node, as would 'peaceful' and 'peace'. This would reduce the number of nodes and probably more clearly expose any relationships.

The code to construct these was written with Processing and makes use of the excellent Traer Physics library.