Sentiment Analysis to Track Emotion and Polarity in Student Fora

Abstract

The purpose of this paper is to propose a data mining methodology for analysing data relating to the participation of students in the online forum of a postgraduate course at the Hellenic Open University. Data is migrated to MongoDB, a NoSQL database management system, and analysed using the rmongodb package of R statistical environment. We focus in sentiment analysis to extract the emotional knowledge of students' fora. Polarity and emotion are identified in messages and are classified as positive, negative or neutral. Messages are categorized and visualized in six basic emotions, as a multiclass approach in understanding students' written opinion. By identifying sentiment behaviour from students' discussion fora, we are able to assess the effectiveness of the learning environment to improve students' learning experience, tutors' instructional experience and the university's institutional strategic view.