To build on Stéfan Sinclair’s plenary talk at DHSI yesterday afternoon, I thought it appropriate to put Voyeur into action with some born-digital EMiC content. Perhaps one day someone will think to produce a critical edition of EMiC’s Twitter feed, but in the meantime, I’ve used a couple basic digital tools to show you how you can take ready-made text from online sources and plug it into a text-analysis and visualization tool such as Voyeur.

I started with a tool called Twapper Keeper, which is a Twitter #hashtag archive. When we were prototyping the EMiC community last summer and thinking about how to integrate Twitter into the new website, Anouk had the foresight to set up a Twapper Keeper hashtag archive (also, for some reason, called a notebook) for #emic. From the #emic hashtag notebook at the Twapper Keeper site, you can simply share the archive with people who follow you on Twitter or Facebook, or you can download it and plug the dataset into any number of text-analysis and visualization tools. (If you want to try this out yourself, you’ll need to set up a Twitter account, since the site will send you a tweet with a link to your downloaded hashtag archive.) Since Stéfan just demoed Voyeur at DHSI, I thought I’d use it to generate some EMiC-oriented text-analysis and visualization data. If you want to play with Voyeur on your own, I’ve saved the #emic Twitter feed corpus (which is a DH jargon for a dataset, or more simply, a collection of documents) that I uploaded to Voyeur. I limited the dates of the data I exported to the period from June 5th to early in the day on June 9th, so the corpus represents  the #emic feed during the first few days of DHSI. Here’s a screenshot of the tool displaying Twitter users who have included the #emic hashtag:

#emic hashtag Twitter feed, 5-9 June 2010

As a static image, it may be difficult to tell exactly what you’re looking at and what it means. Voyeur allows you to perform a fair number of manipulations (selecting keywords, using stop word lists) so that you can isolate the information about word frequencies within a single document (as in this instance) or a whole range of documents. As a simple data visualization, the graph displays the relative frequency of the occurrence of Twitter usernames of EMiCites who are attending DHSI and who have posted at least one tweet using the #emic hashtag. To isolate this information I created a favourites list of EMiC tweeters from the full list of words in the #emic Twitter feed. If you wanted to compare the relative frequency of the words “emic” and “xml” and “tei” and “bunnies,” you’d could either enter these words (separated by commas) into the search field in the Words in the Entire Corpus pane or manually select these words by scrolling through all 25 pages. (It’s up to you, but I know which option I’d choose.) Select these words and click the heart+ icon to add them to your favourites list. Then make sure you select them in the Words within the Document pane to generate a graph of their relative frequency. If want to see the surrounding context of the words you’ve chosen, you can expand the snippet view of each instance in the Keywords in Context pane.

Go give it a try. The tool’s utility is best assessed by actually playing around with it yourself.  If you’re still feeling uncertain about how to use the tool, you can watch Stéfan run through a short video demo.

While you’re at it, can you think of any ways in which we might implement a tool such as Voyeur for the purposes of text analysis of EMiC digital edtions? What kinds of text-analysis and visualization tools do you want to see integrated into EMiC editions? If you come across something you really find useful, please let me know (dean.irvine@dal.ca). Or, better, blog it!