## 6 Conclusions  
This article presented several hybrid models, whose idea is using the Lexicon and Machine Learning approach to analyze emotions in user comments, specifically to detect aggression in texts written in Spanish. 5 approaches are proposed to create different models: Lexicon, TF_IDF_Lexicon, WE_Lexicon, WE_Lexicon_TF-IDF, and the Ensemble approach, which differentiate mainly in the way of extracting the feature vector from the text. The 2 TF-DF and Word Embedding approaches are also implemented, which do not use Lexicons, to compare them with the other models. In each one of the models created, the best hyperparameters are sought from the training dataset of each corpus using GridSearchCV, to then perform experimentation on the test datasets and, through this, compare the results obtained in each model and select the best models in each one of the corpora. The models that obtained the best results use approaches that mix Word Embedding, Lexicons, and ML classifiers, outperforming the base models. The results indicate that hybrid models obtain the best results in the 3 corpora, over the models implemented that do not use Lexicons. This shows that, by mixing the approaches, the aggressiveness detection improves. It is worth highlighting that hybrid models have a better performance in the Chilean corpus, because the Lexicons have a better coverage or coincidence with Spanish words used in Chile, than what occurs with the Spanish used in Mexico. On the other hand, all the models that obtain better results in the corpora use the Support Vector Machine as a classifier. Using the experiments that were run, it can be reasserted that this is one of the best algorithms to perform aggressiveness classification compared to the other algorithms used. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Model** & **F-Measure** & **Accuracy** \\ \hline Chilean & WE\_Lexicon\_SVM & 0.8908 & 0.8920 \\ Mexican & WE\_Lexicon\_TF-IDF\_SVM & 0.8394 & 0.8431 \\ Chilean-Mexican & WE\_Lexicon\_TF-IDF\_SVM & 0.8507 & 0.8548 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Models with the best performance in the corpus. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Corpus**} & \multirow{2}{*}{**Best Model**} & **Best Model TF-IDF** & \multicolumn{1}{c}{**Best Model**} \\  & & **Approach** & **Word Embedding** \\ \cline{2-4}  & & **F-Measure** & \\ \hline Chilean & 0.8908 & 0.8701 & 0.8547 \\ Mexican & 0.8394 & 0.8225 & 0.7831 \\ Chilean-Mexican & 0.8507 & 0.8424 & 0.7900 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: F-measure comparison of the best models with the best base models. Finally, a web application was created, that allows showing the applicability of the proposed models, allowing classifying tweets or comments, evaluating the models implemented, and receiving user feedback on the prediction of the models, that allows generating a database for future research. It is worth mentioning that the backend of the web application is implemented as an API, meaning it can be used by external services. In future work, incorporating Mexican words into the different Lexicons used is considered, especially in the bad words one, to check whether the performance of the models implemented on the Mexican corpus improves. Likewise, using different dictionary type Lexicons is considered, as these include more words than the Lexicon used in this work. The intention is also to implement the management of quantifiers, negations, and emojis in text preprocessing, as this work does not consider these. It is also considered important to incorporate other Ensemble models in the experimentation, using different ML classifiers. While it is felt that it is important to incorporate models based on neural networks in the future, to classify and mix these results with Lexicon-based models. Conceptualization, M.L.-F., A.S.-N. and C.V.-C.; Data curation, M.L.-F.; Formal analysis, M.L.-F., A.S.-N. and C.V.-C.; Investigation, M.L.-F., A.S.-N., C.V.-C. and C.M.-A.