  In recent years, sentiment analysis in social media has attracted a lot of
research interest and has been used for a number of applications.
Unfortunately, research has been hindered by the lack of suitable datasets,
complicating the comparison between approaches. To address this issue, we have
proposed SemEval-2013 Task 2: Sentiment Analysis in Twitter, which included two
subtasks: A, an expression-level subtask, and B, a message-level subtask. We
used crowdsourcing on Amazon Mechanical Turk to label a large Twitter training
dataset along with additional test sets of Twitter and SMS messages for both
subtasks. All datasets used in the evaluation are released to the research
community. The task attracted significant interest and a total of 149
submissions from 44 teams. The best-performing team achieved an F1 of 88.9% and
69% for subtasks A and B, respectively.
