Yesterday I came across a really amazing art/conceptual website called We Feel Fine that automatically collects emotions expressed on the web in blog posts and presents them in a really nice Flash-like interface.
As you can see above it shows many ‘feelings’ (posts from blogs, twitter etc) like animated stars in the universe. If you click on one it reveals the full feeling sentence and the author information. The example above shows feelings from Japan – it is possible to drill down by City, Age, Location, Weather, Male/Female etc.
There is an interview with the authors of the site on .net magazine.
From their ‘mission’ statement:
Since August 2005, We Feel Fine has been harvesting human feelings from a large number of weblogs. Every few minutes, the system searches the world’s newly posted blog entries for occurrences of the phrases “I feel” and “I am feeling”. When it finds such a phrase, it records the full sentence, up to the period, and identifies the “feeling” expressed in that sentence (e.g. sad, happy, depressed, etc.). Because blogs are structured in largely standard ways, the age, gender, and geographical location of the author can often be extracted and saved along with the sentence, as can the local weather conditions at the time the sentence was written. All of this information is saved.
The result is a database of several million human feelings, increasing by 15,000 – 20,000 new feelings per day. Using a series of playful interfaces, the feelings can be searched and sorted across a number of demographic slices, offering responses to specific questions like: do Europeans feel sad more often than Americans? Do women feel fat more often than men? Does rainy weather affect how we feel? What are the most representative feelings of female New Yorkers in their 20s? What do people feel right now in Baghdad? What were people feeling on Valentine’s Day? Which are the happiest cities in the world? The saddest? And so on.
The interface to this data is a self-organizing particle system, where each particle represents a single feeling posted by a single individual. The particles’ properties – color, size, shape, opacity – indicate the nature of the feeling inside, and any particle can be clicked to reveal the full sentence or photograph it contains. The particles careen wildly around the screen until asked to self-organize along any number of axes, expressing various pictures of human emotion. We Feel Fine paints these pictures in six formal movements titled: Madness, Murmurs, Montage, Mobs, Metrics, and Mounds.
I was preparing to create a new feature for my company’s intranet that looks up a products unique ID codes based on the specification information you input. For example, you input the model name and size, and it gives you a list of possible matches. I decided I would use AJAX to do this and was studying some AJAX libraries to use such as YUI, jQuery, Dojo and Prototype. While studying these, I randomly clicked on the profile of one of the developers, and then followed through this which was one of the developers web projects. Nice bit of serendipity.
I then found a related site called Twistori, which is simpler but perhaps all the better for it.
This one constantly streams feelings on the screen. The visual effect is really nice and I think it has great potential for use in clubs and bars as wall projections to create a visual effect and to stimulate conversation.
You can only select from 6 options: Feel, Think, Believe, Wish, Hate and Love. Changing the option immediately switches to feelings using that word. It is just like turning the dial of an analogue radio.
I would like them to amend this site a little so you can choose a city/country. If I had a bar in London, I could then choose the London channel and put it on a wall display. Or, they could use news items. For example during a big Champions League match you could select the match from the list and feed the comments on the game alongside the big screen showing the match. It would be like the ‘fans commentary’ audio sub-channel that Sky TV offer on the big football games, but in visual format.