# Measuring the happiness of large-scale written expression: Songs, Blogs, and Presidents

## TL;DR

This paper introduces a method to quantify happiness in large-scale texts using human assessments, revealing trends in song lyrics and blogs over decades and demographics.

## Contribution

It presents a transparent, scalable approach for measuring emotional states in diverse large-scale texts beyond simple categorization.

## Key findings

- Song lyrics happiness declined from 1960s to 1990s
- Blog happiness increased from 2005 to 2009
- Happiness varies with blogger age and geographic location

## Abstract

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.