This paper describes the process of creating an opinion-mining module that uses deep learning techniques to detect the positive or negative polarity of students' opinions regarding the exercises they solve in an intelligent learning environment (ILE) for the Java language, as well as the detection of learning-centered emotions such as engagement, boredom, and frustration. The information serves as the basis for administrators and teachers who use the ILE to analyze the opinions in order to improve the pedagogy of the ILE exercises. To determine the effectiveness of the deep learning model, we carried out experiments with ten different architectures using the Yelp dataset and one of its own named SentiText containing 147,672 and 10,834 balanced sentences, respectively. We obtained encouraging results with a model that combines a Convolutional Neural Network and a Long Short-Term Memory with an accuracy of 84.32% and an error rate of 0.24 for Yelp and 88.26% and an error rate of 0.33% for SentiText.

deep learning, sentiment analysis, opinion mining, intelligent learning environments