## 5 Discussion
Figure 15 shows the performance of the models in the F-measure metric in the 3 corpora used. It is seen that the model that obtains the best performance in this metric in the Chilean corpus is WE_Lexicon_SVM, with 0.8908. For the Mexican and the Chilean-Mexican corpora, it is the WE_Lexicon_TF-IDF_SVM, with 0.8394 and 0.8507, respectively. On the other hand, Figure 16 shows that the model with the best performance in the Accuracy metric for the Chilean corpus is WE_Lexicon_SVM, with 0.892. For the Mexican and Chilean-Mexican corpora, it is the WE_Lexicon_TF-IDF-SVM, model with 0.8431 and 0.8548, respectively. In general terms, it is seen that the models, in the different metrics, have a similar behavior. Meanwhile, the models generally have a better performance in the Chilean corpus, followed by the Chilean-Mexican one, and finally, the Mexican one. In the graphs, it is seen that the models with a hybrid approach have a better performance compared to the approaches that do not use Lexicons in the Chilean corpus, followed by the Mexican one, and finally, the Chilean-Mexican one. As can be seen in Table 8, the Chilean corpus processed with the 8 hybrid models outperforms the best model that does not use Lexicons. In the case of the Mexican corpus, there are 3 hybrid models that obtain better performance than the best model that does not use Lexicons, as seen in Table 9. Finally, in Table 10, it is seen that only one model outperforms the best hybrid model that does not use Lexicons in the Chilean-Mexican corpus. Figure 15: Comparison of F-measure obtained by the models. As a summary, Table 11 shows the models that obtain the best results according to the F-measure and Accuracy metrics for each corpus used. It is seen that for the Chilean corpus, this is the WE_Lexicon_SVM model, and for the Mexican and Chilean-Mexican corpora, it is the WE_Lexicon_TF-IDF_SVM. These three models use Word Embedding and \begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **F-Measure** \\ \hline WE_Lexicon\_TF-IDF\_SVM & 0.8507 \\ TF-IDF\_SVM (Does not use Lexicons) & 0.8424 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Hybrid models outperforming the best non-Lexicon model in the Chilean-Mexican corpus. Figure 16: Comparison of Accuracy obtained by the models. \begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **F-Measure** \\ \hline WE\_Lexicon\_SVM & 0.8908 \\ WE\_Lexicon\_TF-IDF\_E\_SVM & 0.8851 \\ TF-IDF\_Lexicon\_RF & 0.8839 \\ WE\_Lexicon\_RF & 0.8833 \\ TF-IDF\_Lexicon\_E\_Clf & 0.8828 \\ E\_SVM\_Approach & 0.8804 \\ WE\_Lexicon\_TF-IDF\_SVM & 0.8731 \\ TFIDF\_Lexicon\_E\_SVM & 0.8716 \\ TF-IDF\_RF (Does not use Lexicons) & 0.8701 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Hybrid models outperforming the best non-Lexicon model in the Chilean corpus. Lexicons to extract the features of the texts. This shows that the best results are obtained by incorporating this technique. On the other hand, it is seen that the best result is obtained in the Chilean corpus, achieving a value of 0.89 of F-measure and Accuracy, followed by the Chilean-Mexican corpus, and finally, the Mexican. This can be explained due to the lack of specific Mexican words in the different Lexicons used, especially the bad words Lexicon. Finally, it is seen that the models with the best results used Support Vector Machine as a Machine Learning classifier. With this, it is reasserted that this seems to be a good algorithm to perform the text classification of the three algorithms tested. Table 12 shows the results that obtain the best models of each corpus in the F-measure metric (Table 11), and the models of the base approaches that obtain the best results. It is seen that the broadest difference is found in the Chilean corpus, followed by the Mexican one, and finally, the Chilean-Mexican one. It can also be seen that the difference is broader with the models that use the Word Embedding-based approach.