Analyzing Social Media Data for Better Understanding Students’ Learning Experiences

Abstract

Social media keeps on increasing in size and demands automation in data analysis. Student shares their opinions, concerns, and emotions in the social Web site, because it has a variety of opinions that are central to most of the human activities and a key influence of behavior in their day-to-day life. Many of the tweets made by students have some sort of meaning, but some category does not have a clear meaning such as a long tail. In this paper, a different classification model is developed to analyze student’s comments which are available in social media. This paper mainly focused on emotions. Data is taken from 15,000 tweets of student’s college life and categories—study load of all majors, antisocial, depression, negative emotion, external factors, sleep problems, diversity problems. These multi-label emotional comments are to be classified, analyzed, and compared with the support vector machine and Naive Bayes algorithm to show student learning problems. The experimental results show that major students’ learning problems make better decisions for future education and service to them.