BBC News forum posts: 2,594,745 comments from selected BBC News forums and > 1,000 human classified sentiment strengths with a postive strength of 1-5 and a negative strength of 1-5. The classification is the average of three human classifiers.
Digg post comments: 1,646,153 comments on Digg posts (typically highlighting news or technology stories) and > 1,000 human classified sentiment strengths with a postive strength of 1-5 and a negative strength of 1-5. The classification is the average of three human classifiers.
MySpace (social network site) comments: six sets of systematic samples (3 for the US and 3 for the UK) of all comments exchanged between pairs of friends (about 350 pairs for each UK sample and about 3,500 pairs for each US sample) from a total of >100,000 members and > 1,000 human classified sentiment strengths with a postive strength of 1-5 and a negative strength of 1-5. The classification is the average of three human classifiers.
SentiStrength estimates the strength of positive and negative sentiment in short texts, even for informal language. It has human-level accuracy for short social web texts in English, except political texts.
K. Ganesan, C. Zhai, and J. Han. Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), page 340--348. Beijing, China, Coling 2010 Organizing Committee, (August 2010)