## 4 Discussion  
The results achieved indicates a slightly increase of the accuracy of char-gram over word-grams. We observed that the use of short char-grams improves the results from larger values of \(N\). Regarding the corpus size, the best accuracies were obtained by the corpus-2 and corpus-3, composed respectively by 200 and 400 tweets. As mentioned above, tweets were ordered by agreement among the users. Therefore, this fact remarks that it is more important to have a consensual corpus rather than a larger number of instances, which provides better accuracy in less time.  
Information gain measures the relevance for each feature for the class prediction. This value is the key-factor which RF uses to decide when a new branch has to be created. In addition, information gain provides what are the most informative word-grams and char-grams. This metric could be used to understand the behaviour of the model and allows to perform adjustments for avoiding some features  
Figure 12: Comparison of the accuracy between [5, 6, 7], [6, 7, 8] and [5, 6, 7, 8] char-grams for the RF classifier.  
Figure 13: Comparison of the accuracy between [5, 6, 7], [6, 7, 8] and [5, 6, 7, 8] char-grams for the SMO classifier.  
which may cause over-fitting. Accordingly, we obtained the unigrams, bigrams and trigrams which achieved better information gain. The ten best-scored features are shown in Figure 14. However, as we observed during the extraction of information gain, char-grams tend to provided very similar characteristics that may over-fit tweets with words that are used regularly.  
Figure 14 shows shows that unigrams appear more frequently in the first 10 positions. Specifically, those unigrams are contra (preposition, against), prevencion (substantive, prevention), casos (substantive, cases) and para preposition, for. Finding prepositions, such as contra or para suggests that a large number of positive or negative texts are phrases that express concrete and specific action. Therefore, we calculated the mean and the standard deviation of this prepositions and discover that the preposition para was used 3.23 more times of mean for positive than negative tweets. For the para preposition, the results showed a proportion of 3.23:1 between positive and negative tweets and for the contra preposition, the proportion was 5.75:1. The substantive casos, which may be used for reporting specific outbreak cases, appears mostly in negative utterances with a proportion of 1:5.5.  
Concerning char-grams, the ten best-scored features are shown in Figure 15.  
The results shown in Figure 15 indicate that similar char-grams are interchangeable. For example, we can observe that the char-gram preven, which is a lemma for the substantive prevencion (prevent), and for the verb prevenir (to prevent) appears on the top with equal relevance.  
Figure 14: Information gain for the best ten unigrams, bigrams and trigrams of the word-gram model.  
Figure 15: Information gain for the best five-, six-, seven- and eight-char-grams.  
Char-gram model solve some of the drawbacks identified for the word-gram models (see Section 2.2). However, char-grams produces a high number of similar features so, for real-time applications, we will require of a feature selection process in order to identify the ones that are most effective for a specific machine-learning task. Word-grams, on the other hand, can produce less number of features with the proper usage of cut-off filters to avoid irrelevant features and these features requires less post-processing.