“Analyzing students activities and experiences by mining social media data”

Abstract

Student informal deliberations on online network hut glow keen on top of their instructive experience.
Their opinion, emotion, and concern about the knowledge process. Information on or after such uninstrumented
ecological can give precious acquaintance to update student learning. Analyzing down such data, on the other
hand, can be difficult. The involvedness of student experience reflects beginning social media matter require
human transformation. Then again, the on the increase size of data demands usual data analysis methods. In this
paper, we urbanized work procedure to integrate both qualitative psychiatry and large-scale data mining. We
concentrated on engineering students' Twitter post to understand difficulty and issue in their instructive
experience. We originally conducted a qualitative psychoanalysis on sample taken as of around 25,000 tweets
related to engineering students’ life.