Sentiment Analysis of students reviews on ELearning platforms

Abstract

E-learning is becoming one of the most effective training approaches nowadays. Thanks to the E-Learning platforms and their collaboration tools, students can interact with each other and share their doubts on certain subjects through online reviews and forums. Nowadays E-Learning platforms endow for students to review courses. However, teachers often remain outside this process and are sometimes, not aware about the learning problems encountered in their classes. The solution could be adopting a sentiment analysis methodology to students reviews in order to detect the mood and also the issues during the learning process. That will guarantee problem solving and the confidentiality of communications between students. Thus, affecting beneficially the students experience as a whole in E-Learning platforms In this thesis we demonstrated the attempt of using sentiment analysis on E-Learning reviews to detect E-Learning students problems. In particular we focused on proposing a feedback method guaranteeing the confidentiality of communication and helping to improve of the E-Learning experience. Keywords: E-Learning, Sentiment Analysis, Opinion mining, Machine Learning, Data Processing, Data Filtration.