RESUMO
Nowadays, during the teaching-learning process, it is prevalent
for students to seek information about the subjects to complement
studies or execute school work. For this, it is possible to use search
engines on the Internet, but the texts do not always have the complexity
compatible with the student’s level of education. A solution
for this purpose is the automatic filtering of texts, where computational
models are created for textual classification according to
their contexts. Many studies have presented solutions based on
Machine Learning techniques, but there is no comparison nor an
analysis considering these methods. This work explores this scenario
throughout implementing machine learning algorithms. In the
solution, Natural Language Processing techniques are applied in
order to select characteristics of the textual contents and introduce
them into the classification pipeline. Experiments were conducted
and the results obtained suggest that, machine learning algorithms
have a good predictive performance values, with class-balanced
accuracy of around 0.89 in the test data.