## 1 Introduction
### Background  
Through a revision of the literature, works were studied that propose some aggression detection mechanism. As has already been mentioned, most of these works were applied to texts in English [8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. Regarding the approach used, more than 50% of the articles used ML techniques, using different corpora to train classifiers. The most typically used algorithms are Naive Bayes, Support Vector Machine and Random Forest, due to their widespread use in text classifications. There is a smaller body of works that combine, in one way or another, the ML approach with the use of lexicons, for example, to have predefined lists of bad words that, once detected, are used as features in ML [13; 15; 17]. In [17] was used exclusively the lexicon-based approach, including 9 bad words chosen by the authors considering their high frequency in situations labeled as Cyberbullying, applying a morphological analysis and information recovery techniques to determine the degree of aggression.  
A growing interest can be seen in applying aggression detection in Spanish texts. In [18], 3 corpora are created from Twitter: small corpus (25,304 tweets), medium corpus (229,801 tweets), and big corpus (960,578 tweets). The \"Presumed cyberbullying\" or \"Without cyberbullying\" labelling of each tweet is done automatically, bearing in mind the \"General insult inventory\" [19], as well as adding Ecuadorian insults and the patterns detected in [20]. The model is created using solely ML (Naive Bayes, Support Vector Machine and Logistic Regression) algorithms, and the feature vector is formed using the TF-IDF technique. On evaluating the model with the different corpora, there is an average accuracy of between 80% and 91%, with Support Vector Machine obtains the best result on being applied in the medium corpus, with a peak of 94% accuracy. In addition, a web application is implemented, where the percentage of cyberbullying can be evaluated in real-time in Twitter under 3 scenarios: phrase analysis, analysis of a Twitter profile (bearing in mind the most recent tweets), and trend analysis.  
In [21] was present a proposal that analyzes Peruvian phrases. In this work, a Naive Bayes classifier is trained through the NTLK library [22] for Python, using a lexicon [23] and 595 words labeled manually. The Bag of Words method is used to represent the phrases, through a collection of bad words. The model provides the probability that a phrase contains bullying characteristics.  
The workshop organized by IberEval has provided important room for aggression detection initiatives in Spanish texts [24]. In this event, the participants faced the challenge of proposing models that allow detecting aggression in a corpus which, in its 2018 version, comprised 10,856 instances (7700 for training and 3153 for evaluation). The corpus gathered tweets of Mexican users, in Spanish. The participants proposed a variety of methodologies, which comprised content-based features (frequencies, scores, POS, specific elements of Twitter, etc.), as well as classical ML (Naive Bayes, SVM, Logistic regression, etc.) algorithms, and Neural Networks. In [25] was presented the winning model that reached an average F-measure of 0.620 and an accuracy of 0.667. It uses a classifier that utilizes a Support Vector Machine and two lexicons, and through genetic programming, it makes the final prediction. In 2019, the winning work extracts features using Word Embeddingand n-grams, and uses Multilayer Perceptron to classify [26]. The winning team of the 2020 workshop uses a classifier trained to predict aggression, with majority and weighted vote schemes [27]. The training is done by adjusting the model. Table 2 summarizes the works analyzed that make aggression detection in Spanish, indicating the approach, corpus, algorithms, and the features vector reported.