Emotion and Associated Topic Detection for Course Comments in a MOOC Platform

Massive Open Online Course (MOOC) has been drawn much attention from learners and teachers through the world. MOOC offers a variety of interactive ways, in which the course comment panel is used for express students' opinions and feelings. These comments generally contain some learning problems, attitudes towards the course or the platform support, etc. The feedback information is beneficial for the exchange of ideas among teachers, learners and educational administrators. However, it is quite time-consuming to analyze these important opinions entirely by artificial reading. It is imperative that the MOOC needs the machine learning methods to detect the emotions and topics in text data. In this paper, we propose an application framework and design scheme of intelligent system for the emotion recognition and topic mining, aiming at conducting the intelligent and personalized learning analytics on MOOC. The purposes of the intelligent comment mining system include (1) predicting popularity level of each course, (2) obtaining emotion-topic feedbacks about content of courses for teachers to analyze and improve their teaching strategies, (3) obtaining emotion-topic feedbacks about platform support for administrators to improve user experiences in platform.