  Students' perception of classes measured through their opinions on teaching
surveys allows to identify deficiencies and problems, both in the environment
and in the learning methodologies. The purpose of this paper is to study,
through sentiment analysis using natural language processing (NLP) and machine
learning (ML) techniques, those opinions in order to identify topics that are
relevant for students, as well as predicting the associated sentiment via
polarity analysis. As a result, it is implemented, trained and tested two
algorithms to predict the associated sentiment as well as the relevant topics
of such opinions. The combination of both approaches then becomes useful to
identify specific properties of the students' opinions associated with each
sentiment label (positive, negative or neutral opinions) and topic.
Furthermore, we explore the possibility that students' perception surveys are
carried out without closed questions, relying on the information that students
can provide through open questions where they express their opinions about
their classes.
