## 3 Methods
### Char-Grams Models
\begin{table}
\begin{tabular}{l r r r r r r r r r} \hline \hline \multicolumn{6}{c}{**Combination of Five- and Six- Char-Grams**} \\ \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 & 79.70 & 78.00 & 77.70 & 78.00 \\ corpus-2 & 89.00 & 88.50 & 88.50 & 88.50 & 83.60 & 83.50 & 83.50 & 83.50 \\ corpus-3 & 90.70 & 90.50 & 90.50 & **90.50** & 89.30 & 89.30 & 89.20 & **89.30** \\ corpus-4 & 90.40 & 90.40 & 90.40 & 90.40 & 89.10 & 89.10 & 89.10 & 89.10 \\ corpus-5 & 89.30 & 89.30 & 89.30 & 89.30 & 88.70 & 88.70 & 88.70 & 88.70 \\ corpus-6 & 87.70 & 87.70 & 87.70 & 87.70 & 86.70 & 86.70 & 86.70 & 86.70 \\ corpus-7 & 80.50 & 80.50 & 80.50 & 80.50 & 80.80 & 80.70 & 80.70 & 80.70 \\ \hline \multicolumn{6}{c}{**Combination of Six- and Seven-Char-Grams**} \\ \hline  & \multicolumn{3}{c}{**RF**} & \multicolumn{3}{c}{**SMO**} \\ \hline
**Set** & **P** & **R** & **F1** & **ACC** & **P** & **R** & **F1** & **ACC** \\ corpus-1 & 81.00 & 81.00 & 81.00 & 81.00 & 73.10 & 71.00 & 70.30 & 71.00 \\ corpus-2 & 88.60 & 88.00 & 88.00 & 88.00 & 84.60 & 84.50 & 84.50 & 84.50 \\ corpus-3 & 89.90 & 89.80 & 89.70 & **89.80** & 88.80 & 88.80 & 88.70 & **88.80** \\ corpus-4 & 88.00 & 88.00 & 88.00 & 88.00 & 87.90 & 87.90 & 87.90 & 87.90 \\ corpus-5 & 86.50 & 86.40 & 86.40 & 86.40 & 87.60 & 87.60 & 87.60 & 87.60 \\ corpus-6 & 85.70 & 85.70 & 85.70 & 85.70 & 85.70 & 85.70 & 85.70 & 85.70 \\ corpus-7 & 78.00 & 77.90 & 77.90 & 77.90 & 79.30 & 79.20 & 79.20 & 79.20 \\ \hline \multicolumn{6}{c}{**Combination of Seven- and Eight-Char-Grams**} \\ \hline  & \multicolumn{3}{c}{**RF**} & \multicolumn{3}{c}{**SMO**} \\ \hline
**Set** & **P** & **R** & **F1** & **ACC** & **P** & **R** & **F1** & **ACC** \\ corpus-1 & 79.10 & 79.00 & 79.00 & 79.00 & 67.90 & 67.00 & 66.60 & 67.00 \\ corpus-2 & 85.90 & 85.00 & 84.90 & 85.00 & 82.50 & 82.50 & 82.50 & 82.50 \\ corpus-3 & 88.20 & 88.00 & 88.00 & **88.00** & 88.30 & 88.30 & 88.20 & **88.30** \\ corpus-4 & 86.40 & 86.40 & 86.40 & 86.40 & 86.90 & 86.90 & 86.90 & 86.90 \\ corpus-5 & 85.50 & 85.40 & 85.40 & 85.40 & 84.50 & 84.50 & 84.50 & 84.50 \\ corpus-6 & 82.80 & 82.70 & 82.60 & 82.70 & 84.90 & 84.80 & 84.70 & 84.80 \\ corpus-7 & 75.30 & 74.90 & 74.90 & 74.90 & 77.10 & 76.80 & 76.70 & 76.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results using pair combinations of fixed-length characters for the char-gram model. Figure 9: Comparison of the accuracy between five-, six-, seven- and eight-char-grams for the SMO classifier. Regarding the machine-learning classifiers, RF improves the results in [5; 6] char-grams and [6; 7] char-grams. However, with [7; 8] char-grams is SMO the classifier that achieves better results. As we can expect, the difference between SMO and RF with smaller corpus are huge. However, the results have not worsened from the previous experiment where char-grams were applied without combinations. The progression of the accuracy of combinations of pairs of char-grams for the RF model is shown in Figure 10 and for the SMO model in Figure 11, where we can observe that the accuracy varies significantly among the corpus size for the SMO classifier. For example, combination of [7; 8] char-grams only achieves an 67.00% of accuracy with the corpus-1, but increases until an 82.50% of accuracy with only 100 more tweets corpus-2; however, RF behaves more regularly with less instances besides it reaches slightly less better accuracy than SMO. Finally, we measured the performance of combinations of [5; 6; 7], [6; 7; 8] and [5; 6; 7; 8] char-grams. The results are available in Table 7.  
Figure 11: Comparison of the accuracy between [5; 6], [6; 7], [7; 8] char-grams for the SMO classifier. Figure 10: Comparison of the accuracy between [5; 6], [6; 7], [7; 8] char-grams for the RF classifier. The results in Table 7 highlights that the combinations of all fixed-length results in the highest accuracy for the RF classifier with a 90.30% with the corpus-3.