Mining of Educational Opinions with Deep Learning
Ramón Zatarain Cabada (Tecnológico Nacional de México/ I.T. Culiacán, Mexico)
María Lucía Barrón Estrada (Tecnológico Nacional de México/ I.T. Culiacán, Mexico)
Raúl Oramas Bustillos (Tecnológico Nacional de México/ I.T. Culiacán, Mexico)
Abstract: 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.
Keywords: deep learning, intelligent learning environments, opinion mining, sentiment analysis
Categories: I.2.6, I.2.7, L.3