## 5 Discussion  
Graphs are presented for each metric used as a means of visually comparing the performance of the models of each approach in the different corpora. The models are \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Corpus**} \\ \hline \multirow{2}{*}{**Approach**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Chilean**} & \multirow{2}{*}{**Mexican**} & \multirow{2}{*}{**Chilean-Mexican**} \\ \cline{3-3} \cline{5-5}  & & & & **F-Measure** \\ \hline \multirow{3}{*}{TF-IDF} & TF-IDF\_SVM & 0.8671 & **0.8225** & 0.8225 \\  & TF-IDF\_NB & 0.7514 & 0.7336 & 0.7351 \\  & TF-IDF\_RF & **0.8701** & 0.8069 & **0.8330** \\ \hline \multirow{3}{*}{Lexicon} & Lexicon\_SVM & 0.8535 & 0.5735 & 0.6528 \\  & Lexicon\_NB & 0.8321 & **0.6408** & 0.6865 \\  & Lexicon\_RF & **0.8627** & 0.6347 & **0.7016** \\ \hline \multirow{3}{*}{TF-IDF\_Lexicon} & TF-IDF\_Lexicon\_SVM & 0.8648 & **0.8330** & **0.8372** \\  & TF-IDF\_Lexicon\_NB & 0.7829 & 0.7420 & 0.7543 \\  & TF-IDF\_Lexicon\_RF & **0.8839** & 0.7960 & 0.8231 \\ \hline \multirow{3}{*}{WordEmbedding} & WordEmbedding\_SVM & **0.8547** & **0.7831** & **0.7900** \\  & WordEmbedding\_NB & 0.8253 & 0.7504 & 0.7633 \\  & WordEmbedding\_RF & 0.8170 & 0.7296 & 0.7252 \\ \hline \multirow{3}{*}{WE\_Lexicon} & WE\_Lexicon\_SVM & **0.8908** & **0.7874** & **0.8086** \\  & WE\_Lexicon\_SVM & 0.8495 & 0.7551 & 0.7676 \\  & WE\_Lexicon\_RF & 0.8833 & 0.7107 & 0.7257 \\ \hline \multirow{3}{*}{WE\_Lexicon\_TF-IDF} & TF-IDF\_SVM & **0.8731** & **0.8394** & **0.8507** \\  & TF-IDF\_NB & 0.7842 & 0.7420 & 0.7537 \\ \cline{1-1}  & TF-IDF\_RF & 0.8501 & 0.7061 & 0.7033 \\ \hline \multirow{3}{*}{Ensemble} & TF-IDF\_Lexicon\_E\_C\_Ifs & 0.8828 & 0.8191 & **0.8399** \\ \cline{1-1}  & TF-IDF\_Lexicon\_E\_SVM & 0.8716 & **0.8308** & 0.8356 \\ \cline{1-1}  & WE\_Lexicon\_TF-IDF\_E\_SVM & **0.8851** & 0.7879 & 0.8219 \\ \cline{1-1}  & E\_SVM\_Approaches & 0.8804 & 0.7868 & 0.8146 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: F-measure obtained by the models in the corpus. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Corpus**} \\ \hline \multirow{2}{*}{**Approach**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Chilean**} & \multirow{2}{*}{**Mexican**} & \multirow{2}{*}{**Chilean-Mexican**} \\ \cline{3-3} \cline{5-5}  & & & & **Accuracy** \\ \hline \multirow{3}{*}{TF-IDF} & TF-IDF\_SVM & 0.8690 & **0.8281** & **0.8473** \\  & TF-IDF\_NB & 0.7530 & 0.7522 & 0.7500 \\  & TF-IDF\_RF & **0.8717** & 0.8204 & 0.8418 \\ \hline \multirow{3}{*}{Lexicon} & Lexicon\_SVM & 0.8556 & 0.6977 & 0.7154 \\  & Lexicon\_NB & 0.8367 & 0.6822 & 0.7113 \\  & Lexicon\_RF & **0.8636** & **0.7000** & **0.7211** \\ \hline \multirow{3}{*}{TF-IDF\_Lexicon} & TF-IDF\_Lexicon\_SVM & 0.8663 & **0.8395** & **0.8439** \\  & TF-IDF\_Lexicon\_NB & 0.7813 & 0.7486 & 0.7558 \\  & TF-IDF\_Lexicon\_RF & **0.8852** & 0.8122 & 0.8340 \\ \hline \multirow{3}{*}{WordEmbedding} & WordEmbedding\_SVM & **0.8569** & **0.7972** & **0.8021** \\  & WordEmbedding\_NB & 0.8259 & 0.745 & 0.7599 \\  & WordEmbedding\_RF & 0.8218 & 0.7713 & 0.7616 \\ \hline \multirow{3}{*}{WE\_Lexicon} & WE\_Lexicon\_SVM & **0.8920** & **0.8027** & **0.8184** \\  & WE\_Lexicon\_SVM & 0.8502 & 0.7495 & 0.7664 \\  & WE\_Lexicon\_RF & 0.8852 & 0.7590 & 0.7626 \\ \hline \multirow{3}{*}{WE\_Lexicon\_TF-IDF\_SVM} & WE\_Lexicon\_TF-IDF\_SVM & 0.8744 & **0.8431** & **0.8548** \\  & WE\_Lexicon\_TF-IDF\_NB & 0.7827 & 0.7486 & 0.7548 \\ \cline{1-1}  & WE\_Lexicon\_TF-IDF\_RF & 0.8542 & 0.7590 & 0.7528 \\ \hline \multirow{3}{*}{Ensemble} & TF-IDF\_Lexicon\_E\_C\_Ifs & 0.8839 & 0.8309 & **0.8480** \\ \cline{1-1}  & TF-IDF\_Lexicon\_E\_SVM & 0.8731 & **0.8386** & 0.8435 \\ \cline{1-1}  & WE\_Lexicon\_TF-IDF\_E\_SVM & **0.8866** & 0.8022 & 0.8310 \\ \cline{1-1}  & E\_SVM\_Approach** & 0.8825 & 0.8081 & 0.8289 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy obtained by the models in the corpus. presented in different colors.