  Solicited public opinion surveys reach a limited subpopulation of willing
participants and are expensive to conduct, leading to poor time resolution and
a restricted pool of expert-chosen survey topics. In this study, we demonstrate
that unsolicited public opinion polling through sentiment analysis applied to
Twitter correlates well with a range of traditional measures, and has
predictive power for issues of global importance. We also examine Twitter's
potential to canvas topics seldom surveyed, including ideas, personal feelings,
and perceptions of commercial enterprises. Two of our major observations are
that appropriately filtered Twitter sentiment (1) predicts President Obama's
job approval three months in advance, and (2) correlates well with surveyed
consumer sentiment. To make possible a full examination of our work and to
enable others' research, we make public over 10,000 data sets, each a
seven-year series of daily word counts for tweets containing a frequently used
search term.
