FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings
Table 2: Microblog results with all features on validation and test sets.
['Regressor', 'Set', 'Cosine', 'MAE']
Results in the test set are worse than in the validation set with the exception to MLP. The official score obtained in sub-task 5.1 was 0.6948 using Random Forests (RF), which is the regressor that achieves higher cosine similarity and lower MAE in both training and validation set.