TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification
Table 1: Size of the data used for training and development purposes. We only relied on the SemEval 2016 datasets.
['[EMPTY]', 'Train', 'Development', 'DevTest', 'Test']
Having the features extracted we experimented with several families of classifiers such as linear models, maximum-margin models, nearest neighbours approaches and trees. We evaluated their performance using the data provided by the organisers, which were already split in training, validation and testing parts. From the early validation schemes, we found that the two most competitive models were Logistic Regression from the family of linear models, and Support Vector Machines (SVMs) from the family of maximum margin models.