## 3 Methods
### Word Gram Model
SMO improved slightly the accuracy of RF in the case of unigrams, but the overall behaviour of both models was similar (except when comparing RF and SMO in corpus-1). Different combinations of unigrams, bigrams and trigrams to detect if there is any improvement were also studied. The results for combinations of (1) unigrams and bigrams, (2) bigrams and trigrams, and, finally, (3) unigrams, bigrams and trigrams are shown in Table 4.  
Figure 4: Word-grams: unigrams, bigrams and trigrams of random-forest (RF) classifier per corpora. Figure 5: Word-grams: unigrams, bigrams and trigrams of sequential minimal optimization (SMO) classifier per corpora. The combination of unigrams and bigrams, shown in the first section of Table 4, improved the accuracy obtained by unigrams applying RF. However, this increment was slightly, with 89.00%. With SMO, the accuracy presents a slight reduction with 89.30%. The combinations of unigrams, bigrams and trigrams achieve the best accuracy for the corpus-3 with 89.80% for RF and 88.30% for SMO. The comparison of these results with individual unigrams, bigrams or trigrams presents two differences. On the one hand, RF increased the accuracy from 88.00% to 89.70%. SMO, on the other hand, decreased its accuracy from 90.50% to 88.30%. Compared to bigrams, RF and SMO improved their accuracy, RF with 89.80% and SMO with 88.30%. When compared with trigrams, the best accuracy of both classifiers is reached with the smaller corpus (corpus-1). With respect to the classification algorithms, it can be seen that SMO achieves better results than RF in unigrams in the combination of (1) unigrams and bigrams, (2) bigrams and trigrams. However, RF achieves slightly better accuracy for the combination of unigrams, bigrams and trigrams. In this experiment, some features are dependant for other because we have features to represent the same concepts. For example, the expression El Zika, which stands for the Zika disease will produce the following word-grams: El (The), Zika, El Zika. In this sense, these features are redundant and they do not add relevant information. RF internally selects optimal splits so redundant features should not affect them. However, massive redundancy it could cause that the the redundant feature-set was selected instead better splits decreasing the quality of the model. When comparing SMO and RF with smaller corpus size, we can observe how there is a major difference between them. SMO achieves lower results, specially significantly for the combination of  
\begin{table}
\begin{tabular}{l r r r r r r r r} \hline \hline  & \multicolumn{6}{c}{**Unigrams and Bigrams**} \\ \hline  & \multicolumn{3}{c}{**RF**} & \multicolumn{3}{c}{**SMO**} \\ \hline
**Set** & **P** & **R** & **F1** & **ACC** & **P** & **R** & **F1** & **ACC** \\ \hline corpus-1 & 85.10 & 85.00 & 85.00 & 85.00 & 79.60 & 79.00 & 78.90 & 79.00 \\ corpus-2 & 88.20 & 88.00 & 88.00 & 88.00 & 86.10 & 86.00 & 86.00 & 86.00 \\ corpus-3 & 89.10 & 89.00 & 89.00 & **89.00** & 89.30 & 89.30 & 89.20 & **89.30** \\ corpus-4 & 88.20 & 88.10 & 88.10 & 88.50 & 88.50 & 88.50 & 88.50 \\ corpus-5 & 87.90 & 87.80 & 87.80 & 87.80 & 88.10 & 88.10 & 88.10 & 88.10 \\ corpus-6 & 85.90 & 85.80 & 85.80 & 85.80 & 85.20 & 85.20 & 85.10 & 85.20 \\ corpus-7 & 78.80 & 78.70 & 78.70 & 78.70 & 78.20 & 78.10 & 78.00 & 78.10 \\ \hline \multicolumn{8}{c}{**Bigrams and Trigrams**} \\ \hline
**Set** & **P** & **R** & **F1** & **ACC** & **P** & **R** & **F1** & **ACC** \\ \hline corpus-1 & 67.20 & 67.00 & 66.90 & 67.00 & 68.10 & 68.00 & 67.90 & 68.00 \\ corpus-2 & 77.30 & 76.50 & 76.30 & 76.50 & 76.40 & 76.00 & 75.90 & 76.00 \\  
unigrams and trigrams and the combination of unigrams, bigrams and trigrams. In this sense, as the features have more redundancy, SMO is not capable of perform a good separation of the positive and negative classes. Figures 6 and 7 shows the relationship between the accuracy with the corpus size for the RF and SMO classifiers, respectively.