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In recent years, bullying and cyberbullying have increased, affecting schools, colleges, and universities. Due to advances in information technology, any person is exposed to being attacked. Therefore, it is necessary to create solutions through appropriate techniques that help prevent bullying and cyberbullying. Consequently, this article proposes to create a textual synthesis from survey data that allows the development of models to classify or predict both victims and aggressors of bullying and cyberbullying. Data mining techniques, decision trees, and grouping techniques were used, resulting in a textual synthesis. This allowed the creation and evaluation of a supervised learning model and another model with clustering techniques applied to the data from the surveys performed on university students. The results demonstrated the importance of textual synthesis for generating models for the classification or prediction of victims and aggressors of bullying and cyberbullying, with an accuracy greater than 75% for the grouping model with the best performance.
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