## 2 Materials and Methods  
Figure 1 describes the methodology used in this work. In the first stage, a revision of the literature was made to get to know the models recently applied for aggression  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**Journals**} & \\ \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Approach**} & \multirow{2}{*}{**Corpus**} & \multirow{2}{*}{**Classification**} & \multirow{2}{*}{**Features Vector**} & \multirow{2}{*}{**Best Result**} \\  & & & & & & \\ \hline \multirow{6}{*}{2018} & Automatic cyberbullying & \multirow{2}{*}{machine} & 100 social network & \multirow{6}{*}{naive bayes} & \multirow{2}{*}{bag of words} & 0.93 accuracy \\  & & & & & 0.93 accuracy \\  & & & & & 0.93 accuracy \\  & & & & & 0.93 accuracy \\ \hline \multirow{6}{*}{2019} & Presumptive detection of & \multirow{6}{*}{machine} & own corpus of 960,578, & naive bayes, & \multirow{6}{*}{tf-idf} & support \\  & cyberbullying on twitter & \multirow{6}{*}{machine} & cyberbullying”or & \multirow{6}{*}{without} & machine \& \\  & through natural language & machine & cyberbullying”or & machine \& & \\  & processing and machine & learning in the spanish & \multirow{6}{*}{automatically labeled} & logistic & \multirow{6}{*}{tf-idf} & with a peak \\  & language [18]. & & cyberbullying, & & & of 0.94 in \\  & & & automatically labeled & & & accuracy. \\ \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**Workshops**} & & \\ \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Place**} & \multirow{2}{*}{**Approach**} & \multirow{2}{*}{**Classification**} & \multirow{2}{*}{**Features vector**} & \multirow{2}{*}{**F-measure**} \\  & & & & & & \\ \hline \multirow{6}{*}{2018} & Ingeotec at mex-a3t: & \multirow{6}{*}{1} & Combines machine & \multirow{6}{*}{support vector machine} & \multirow{6}{*}{-} & \multirow{2}{*}{0.620} \\  & author profiling and & & & & & \\ aggressiveness analysis in & \multirow{6}{*}{1} & learning models and & lexicons & & & \\  & & & & & \\  & evomsa [25]. & & & & & \\ \hline \multirow{6}{*}{2019} & \multirow{6}{*}{ \begin{tabular}{c} Attention mechanism for \\ aggressive detection [28]. \\ \end{tabular}  } & \multirow{6}{*}{2} & Neural network & \multirow{6}{*}{neural networks} & word \multirow{6}{*}{\ \begin{tabular}{c} embedding \& \\ presence of bad \\ words or not \\ according to the \\ lexicon \\ \end{tabular}  } & \multirow{6}{*}{0.605} \\  & & & & & \\ \hline \multirow{6}{*}{202019} & \multirow{6}{*}{ \begin{tabular}{c} Uach at mex-a3t 2019: \\ preliminary results on \\ detecting aggressive \\ tweets by adding author \\ information via an \\ unsupervised strategy [26]. \\ \end{tabular}  } & \multirow{6}{*}{1} & \multirow{6}{*}{Machine learning} & multilayer & \multirow{6}{*}{word embedding \&} \multirow{6}{*}{0.620 (mp)} \\  & & & & & \\  
detection in Spanish texts. Then, the corpora to be used in this work was chosen. After this, the hybrid classification models are defined and implemented, considering the literature revision. In the next stage, the models are evaluated through experiments, measuring their performance using the metrics utilized in the literature. Finally, the results analysis of the evaluation is carried out, reaching conclusions on the hybrid models implemented. Table 3 corpora are used, that contain tweets in Spanish, manually labeled as \"aggressive\" and \"non-Aggressive\". The first corpus is from tweets by Chilean users, and for this reason, is called the Chilean corpus. It comprises the corpus prepared by [31], with 1470 tweets in the context of aggression against women, and the corpus prepared in [32] which has 1000 tweets. 41% of the total correspond to tweets labeled as \"aggressive\" and 59% as \"non-Aggressive\". The second corpus used was created by [24]. These are tweets in Spanish collated in Mexico City, and were filtered to use only the instances labeled.