One of the cool things that we've got lying around to play with are a bunch of tweets collected during the start of the Arab Spring. We've used a lot of different techniques for visualizing and analyzing this data, but when the Eclipse Zest Cloudio toolset came along recently, I thought it would be an interesting way to quickly see what exactly people were really talking about. I think the results speak for themselves. Thanks to Stephan Schwiebert for this great contribution. One of the downsides of this approach is that unlike in our graph visualizations, you lose concept proximity. So for example, looking at the cloud you can instantly see that Tunisia and Lebanon are frequently mentioned, but what you can't see is how frequently they are used together. That's what our graph visualization tools do now, and I think there are some neat possibilities for marrying the two approaches.
Butterflyzer work has kicked back into high gear, and I'm currently collecting live streaming data from Twitter on the OccupyWallStreet movement. I can't wait to put together a similar word cloud for it. Obviously, a lot has changed in Egypt in the nine months since this data was collected. Who knows what will have changed here nine months from now?
Methodology: The data was collected from 10,000 tweets between Jan 23 and 29th containing the word "egypt". We then extracted word counts, removing common english words and other noise, and then visualized the remaining words with Cloudio, sizing for frequency of use.
Cool stuff! And interesting you mention that you'd be interested in a combination of tag clouds and graphs - I was thinking about that too, I basically imagined tag clouds as nodes in a graph, e.g. for hierarchical clustering...
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