Ad verba per numeros
mood analysis; needless to say I'm interested on its applications to user generated content, in particular, tweets.Yesterday I had notice of this really interesting paper:
Measuring the Happiness of Large-Scale Written Expression: Songs, Blogs, and Presidents by Peter Sheridan Dodds and Christopher M. Danforth. Journal of Happiness Studies (2009).The importance of quantifying the nature and intensity of emotional states at the level of populations is evident: we would like to know how, when, and why individuals feel as they do if we wish, for example, to better construct public policy, build more successful organizations, and, from a scientific perspective, more fully understand economic and social phenomena. Here, by incorporating direct human assessment of words, we quantify happiness levels on a continuous scale for a diverse set of large-scale texts: song titles and lyrics, weblogs, and State of the Union addresses. Our method is transparent, improvable, capable of rapidly processing Web-scale texts, and moves beyond approaches based on coarse categorization. Among a number of observations, we find that the happiness of song lyrics trends downward from the 1960s to the mid 1990s while remaining stable within genres, and that the happiness of blogs has steadily increased from 2005 to 2009, exhibiting a striking rise and fall with blogger age and distance from the Earths equator.According to the New York Times, their authors are about to launch an online tool applied to Twitter: one happy bird.All in all, an interesting approach, I'm looking forward to give one happy bird a try.