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When you read a book it can feel as if you’re encountering a series of hidden networks–characters who talk to each other, ideas that relate to each other. Our new visualization, the Phrase Net, is designed to bring some of these networks to light.

The image below shows a phrase net for Jane Austen’s novel, Pride and Prejudice. To create this network, we start by choosing a connecting word: “and” The visualization then searches the entire novel for “linking phrases” of the form “word1 and word2″. Each of these phrases defines a connection between two words. The resulting graph shows clusters of related concepts and people.

The size of each word shows how many times it occurred in a linking phrase. The arrows show the order of the occurrences. Darker blue words occurred typically as the first word in the linking phrase, while light blue words occurred in the second position.
Other connectors give different views. The word “at” provides a network of places in the the book.

In addition to a standard menu of phrases, we let you type in your own phrase patterns. Use an asterisk to stand for a word–any pattern with exactly two asterisks will give rise to phrase network. (Advanced users can type in full “regular expressions” as well.). You can read more about the Phrase Net on its description page here.

Since the Many Eyes phrase net is based on simple pattern matching, it certainly doesn’t tell you everything about the underlying network of concepts in a piece of text. But we’ve found it to be a surprisingly interesting tool for viewing books, speeches, and other unstructured documents. We’re curious to hear your comments on this visualization technique–and to see what networks you come up with!

Back in October, we invited the readers of our blog to try out the private beta of a new side-project, Many Eyes Wikified. We’re happy to announce that the Wikified beta is now open to the public!

Some background
We use wikis a lot here at IBM: to track projects, gather links, and draft documents. They can be very useful for casually organizing information.

Meanwhile, we’ve been thinking about reporting and dashboard systems lately. These are specialized business tools that feature several screens with multiple views into a big dataset. They can be extremely powerful, but also complicated to set up and use.

We decided to build a tool that combines the informal appeal of a wiki with some of the visualization capabilities of a dashboard-like system. And so Wikified was born!

What it does

Many Eyes Wikified is a “remix” of Many Eyes, using a wiki markup syntax to enable you to easily edit datasets and lay out visualizations side-by-side.

It also functions just like a normal wiki: you can collaboratively edit pages, add explanations or documentation to your visuals, see a page’s edit history, and revert changes.

Unlike a normal wiki, you can embed content from your blog or other data source within Wikified and visualize it. You can also embed the content you make in Wikified elsewhere, just like you can in Many Eyes:

Feel free to give it a try! You can log in with your existing Many Eyes username and password (although the login process might be a bit different from what you’re used to). Wikified is very much a work-in-progress, so we’re eager to hear your feedback!

In a departure from our usual posts, we’ve got some news about the hidden workhorse of Many Eyes: the server side application. If you’re interested in the technology behind Many Eyes, read on. If not, feel free to skip to the last paragraph to see what this means for the future of the site.

Since November 8th, you’ve been using the new Ruby on Rails version of Many Eyes! You may have used it already if you’ve used the New York Times’ Visualization Lab.

Doing a full rewrite of an application is not something to be taken lightly, but we’re confident that the move to Rails has been well worth it. It’s been a joy to develop, and the site is running faster than ever.

“Faster?” We can almost hear you say. “I heard Ruby was slow!”

While it’s true that Ruby isn’t the fastest executing language around, Ruby is faster in a few important ways. It’s taken less time and fewer lines of code to produce equivalent functionality in Ruby, and it’s been easier to optimize the application. In the end, this means our application actually runs faster.

Last but not least, there have been a few small changes to the site, such as the new dataset tag cloud and the ability to change your display name and profile under “my stuff.”

So what does this mean? It means that Many Eyes is now running on a system that is more flexible and easier to maintain, which will allow us to add new features more quickly. With that in mind, we would love to hear your feedback! What would you like to see on the site next?

From Friday at 6pm EST until Saturday 3pm EST, Many Eyes will down for scheduled maintenance. We’re moving to a new server and doing some data migration. Sorry for any inconvenience this may cause, but we think you’ll enjoy the performance upgrade. It may be back up before then, so feel free to check back early.

We’ll have some more news coming soon, so stay tuned!

Photo credit: Flickr user jphilipg

It’s been a busy Fall here at the Visual Communication Lab, and we’ve got a few tricks up our sleeve this Halloween. One of them is a new side-project of Many Eyes, called Many Eyes Wikified. Wikified can be thought of as a “remix” of Many Eyes, with some powerful new features that explore a novel way to create and lay out visualization dashboards.

We’re not quite ready to launch Wikified to the public yet, but we’d love to know what you think of it. If you’re interested, you can sign up for the closed beta and get a sneak peek at what we’ve got in store.

We are very pleased to announce that Wordle–the world’s most popular word cloud application–is debuting on Many Eyes! The brainchild of computer virtuoso and esteemed colleague Jonathan Feinberg, Wordle launched last month and has taken the Web by storm. Over 100,000 word clouds have been created so far.

Wordle marks a departure from the more analytical visualizations on Many Eyes. Why bring a self-described “toy” to a site for social data analysis? People have reported finding value beyond entertainment in creating these word clouds. Teachers have used Wordles in classrooms as conversation catalysts; others have created them to express their identities, and scholars have used them to visualize the output of statistical explorations of texts.

And Wordle brings serious typographical considerations into a realm where usually the
biggest choice is “serif vs sans-serif.” Part of the appeal of Wordle lies in a number of clever layout and editing capabilities. First off, it breaks the grid of regular tag clouds by treating all white space available (both in-between words and even inside a single word) as fair game for layout. For instance, a small word could end up appearing inside the “O” of a bigger word. Second, it allows words to be written in any given direction: horizontal, vertical, and diagonal. Third, there are choices of fonts, layouts, and colors. And, if all this weren’t enough, it also allows the creator to remove any word she doesn’t want to have on the cloud (right-click a word and say “remove”). Pretty sweet!

So come play with Wordle! We hope this toy brings you some pleasure and, perhaps, some new insights.

Today we’re featuring two fun tutorials that explain how you can go from finding a data set on the web to uploading it to Many Eyes and creating your own visualizations:

1) The classic “FMUV” (Find, Massage, Upload, Visualize) tutorial by our colleague, Jonathan Feinberg.

2) The brand new “Northstar Nerd Tutorial: Data Visualization via IBM’s Many Eyes” by our user Richard Hoeg.

We are grateful to both of them for putting together such cool presentations–thanks for the help!!

If you happen to have your own tutorial, let us know. We’d love to feature more of these.

Speeches by Obama versus speeches by Clinton. Blogs by men versus blogs by women. Song lyrics from the 80s versus lyrics from the 50s. The list of tag clouds on Many Eyes is a study in contrasts.

There’s no question that our users like visualizing the differences between related texts, but making comparisons by looking at one text a time is difficult. Today we’re launching a new version of our tag cloud, which we hope will allow for easier and clearer analyses.

If you want to compare two texts directly, you can merge them (see the instructions for details) and then see a special “interleaved” tag cloud, which will let you compare side by side the relative frequencies of the words in the two texts. You can see an example here:

which shows the US presidential State of the Union address from 2002 and 2003. The 2002 speech is in orange and the 2003 speech is in blue. You can see a number of differences directly: “Afghanistan” shrinks dramatically from 2002 to 2003, and “Saddam” seems to grow in proportion.

Give the new tag cloud a spin! We’re looking forward to seeing what comparisons our users draw next.

We are happy to announce that members of the VCL have two visualizations featured in Design and the Elastic Mind, the new exhibit at the MoMA in New York, which runs from February 24 to May 12, 2008.

History Flow, our visualization of Wikipedia editing history is making an appearance with “chocolate” and “abortion.” Thinking Machine, a visualization of the complex decisions that underlie strategic thinking, reveals the machine’s thought process as it tries to beat you in a game of chess.

The exhibit explores the relationship between science and design in the contemporary world. With over 200 pieces, it spans a variety of fields: from nanodevices to vehicle constructions, from appliances and interfaces to furniture design. Visualization commands one of the six rooms of the exhibit.

If you happen to be in New York City, it’s worth a visit!