The value of diversity in education is highly emphasized in recent years, particularly in the wake of the COVID-19 pandemic, by many scholars. MOOCs have contributed the shift to online learning by expanding the range of available learning opportunities. They have gained popularity, especially in higher education by providing unlimited access to lectures and rich learning materials by renowned and respected academics in a wide variety of areas, with no restrictions and at very low fees. Besides, reasons for enrolling in a MOOC might vary according to the learners’ preferences on its instructional design as well as their emotions. Knowing this, creating more effective online courses that address affective issues would attract a broader spectrum of students and optimize the learning experience.

This study intends to reveal the emotional features of MOOCs to gain a better understanding of why learners choose a specific course among hundreds of alternatives available on MOOC platforms. The study uses Kansei Engineering methodology by enriching it with text analytics algorithms for extracting the learners’ emotions from the user reviews. The research methodology includes the collection of reviews from MOOCs and then the analysis of them through NLP techniques to identify Kansei words characterizing MOOCs, specifically for the courses in the field of Analytical/Quantitative Methods. The expected output of this study is a Kansei corpus for online courses related to the given field.