Twitter has become one of the major platforms for self-expression in the Social
Web, mostly due to its adoption by mobile users and its short message format.
This presents endless possibilities for social behavior researchers that, for
the first time, have access to massive amounts of data generated by humans.
Nevertheless, most of the current research on emotions in social platforms
focuses on reactions to particular events, or crowd behavior. In this article
we present our research in the identification and characterization of user
sentiment profiles in online social media. By analyzing a dataset of more than
36,000 users, we identify several distinctive groups, according to similarities
in their sentiment behavior. We study differences and similarities between
these profile clusters and present detailed statistics. We found that a large
number of Twitter users can be grouped in nine distinct profiles according to
the strength and polarity of their sentiment. Researchers and practitioners can
benefit from our approach to characterize Twitter users in several scenarios,
such as social recommendation, and mood estimation.