Specifically, the team labeled 1.6 million tweets in 13 different languages. Using these annotated tweets as training data, the team built multiple automatic sentiment classification models.

Their experiments resulted in a number of interesting conclusions. Firstly, the researchers state that there is no statistically major difference between the performance of the top classification models. Next, the general accuracy of the classification models does not correlate to performance when applied to the ordered three-class sentiment classification problem. Lastly, they state that it is more efficient to focus on the accuracy of the training data, rather than the type of classification model used. ゛

ive read that paper before it was really interesting higly recommended for beginner data scientists