We Feel Fine – An exploration of human emotions, in six movements.
From the “mission” page:
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.
At its core, We Feel Fine is an artwork authored by everyone. It will grow and change as we grow and change, reflecting what’s on our blogs, what’s in our hearts, what’s in our minds. We hope it makes the world seem a little smaller, and we hope it helps people see beauty in the everyday ups and downs of life.
I mention this as an interesting data set and possible approach to discovering the semantic range in the use of particular terms.
Clearly we use a common enough vocabulary for Google and similar applications to be useful to most people a large part of the time. But they fail with alarming regularly and without warning as well. And therein lies the rub. How do I know that the information in the first ten (10) hits is the most important information about my query? Or even relevant, without hand examining each hit? To say nothing of the “hits” at 100+ and beyond.
The “problem” terms are going to vary by domain but I am curious if identification of domains, along with use of domain based vocabularies, might improve searches, at least of professional literature. Thinking there are norms of usage in professional literature that may make it a “special” case. Perhaps most of the searches of interest to enterprise searchers are “special” cases in some sense of the word.