  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 1960's to the
mid 1990's 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 equator.
