I. Introduction
The advances in technologies have brought great innovations to the field of education, and massive open online courses (MOOCs) have gained momentum as an innovative way of increasing access and equity in education [1]. With the expansion of MOOCs, the number of learners involved is also increasing. Therefore, a large number of unstructured text data have been generated in the course review area, which can reflect learners’ emotional state and learning experience. Even though MOOCs are very popular, the high attrition rate has always been a concern of their owners [2]. In order to help instructors guide and intervene with learners’ emotions, so as to reduce the attrition of MOOCs, it is important to understand learner-generated reviews in the courses and emotion identification could be used as the first step for further adaptive interventions.