Analyzing ELearning platform reviews using Sentimental Evaluation with SVM Classifier

Abstract

In today's scenario, the participation of eLearning courses is rapidly increasing among users, especially during post-pandemic situation. Due to a hike in the need of eLearning, there is a sharp increase in emerging of online courses. Not only the academic people (students and teachers), but others also interested in the online courses, irrespective of their ages. Since numerous eLearning platforms are mushrooming, it is mandatory to follow various standard techniques to evaluate the quality of these courses. It would be more adaptable, if the evaluation techniques are made based on the Course users feedback. These feedback of the users contain more valuable information which helps in constructive decision making. The satisfactions and criticisms of the courses are shared through opinion of the registered user. Due to enormous amount of web reviews available for a course, it is extremely time-consuming and difficult to manually analyze the review and come to a conclusive decision. Reviews contain course feature specific factual information along with the opinion statements which may be positive or negative. The text reviews obtained from most of the webpages of the online courses feedback or comments section, are found to be unstructured and inconclusive. Analyzing and extracting the actual opinion throughout the reviews manually is very difficult. So, an automated technique is needed to evaluate the course based on the features of the courses, which results in attaining a decision. The Aspect based opinion mining is such a methodology which describes the important aspects of each opinion and classify them on their polarity.