This is a timely article, as my current project deals with Twitter sentiment analysis http://www.sentimentview.com http://www.sentimentview.com. Although, I'm

This is a timely article, as my current project deals with Twitter sentiment analysis http://www.sentimentview.com http://www.sentimentview.com. Although, I'm not classifying by specific emotion type as the article describes. In all, I'm seeing accuracy rates of around 80%. This is in comparison to a brute-force word list, which scored an accuracy of 62%. I'd be curious to see what kind of accuracy the researches in the article are achieving, with so many sub-division classifications. Not to mention, how did they derive their initial model?