A Preliminary Study on Students Perception of Online Distance Learning: A Sentiment Analysis on Twitter

Abstract

Online Distance Learning (ODL) is any learning activities in the formal, informal, and non-formal domains aided by information and communication technology, to reduce physical and psychological distance and promote interactivity and communication among learners, learning sources, and facilitators. Sentiment analysis is the process of identifying and extracting subjective information from text using natural language processing and text analysis techniques. However, students’ sentiments about ODL are varied, and many factors may contribute to that. This study aims to identify students’ sentiments about online distance learning experiences by gathering and analyzing Twitter. The sentiment classification model was developed using K-Nearest Neighbor (KNN) technique. The result visualized through the dashboard with different rates of accuracy results for the KNN classifier. We manage to get 94.49% of model accuracy using a confusion matrix.  As a result, this preliminary study can be implied to help the universities to improve the ODL success rate and future decision-making for the ODL process.