Opinion Mining Models for Learner Feedback on Massive Open Online Courses

Feedback and rating in MOOC's (Massive Open Online Courses) are critical components in the creation of courses as well as a successful learning process. These data can be evaluated to discover any probable patterns and, depending on the input, the course is likely to be improved. Recent research in sentiment analysis has shown that it may be used to identify emotions. Positive and negative feedback classification can be used as an initial parameter to determine the learner's degree of satisfaction. The major goal of our work includes investigating various machine learning methods for sentiment analysis utilizing existing MOOC course feedback as a case study. The emotions shown in the feedback might give information about the learner's degree of pleasure. Logistic Regression, KNN, Naïve Bayes, Decision Tree and Random Forest were the methods adopted in the research.