A Simple Approach to Multilingual Polarity Classification in Twitter
Table 3: Datasets details from each competition tested in this work
['[EMPTY]', '[BOLD] Dataset', '[BOLD] Positive', '[BOLD] Neutral', '[BOLD] Negative', '[BOLD] None', '[BOLD] Total']
It can be observed, from the table, the number of examples as well as the number of instances for each polarity level, namely, positive, neutral, negative and none. The training and development (only in SemEval) sets are used to train the sentiment classifier, and the gold set is used to test the classifier. In the case there dataset was not split in training and gold (Arabic, German, Portuguese, Russian, and Swedish) then a cross-validation (10 folds) technique is used to test the classifier. The performance of the classifier is presented using different metrics depending the competition.