Survey on Social Media using Twitter Data for Students Experience using Naïve Bayes Technique

Abstract

Students informal conversations on social media (e.g. Twitter, Face book) shed light into their educational experiences opinions, feelings, and concerns about the learning process. Data from such un-instrumented environments can provide valuable knowledge to inform student learning. Analysing such data, however, can be challenging. In this paper, we developed a workflow to integrate both qualitative analysis and large-scale data mining techniques [1]. We focused on engineering students’ Twitter posts to understand issues and problems in their educational experiences. We first conducted a qualitative analysis on samples taken from about 25,000 tweets related to engineering students’ college life. We found engineering students encounter problems such as heavy study load, lack of social engagement, and sleep deprivation. Based on these results, we implemented a multi-label classification algorithm to classify tweets reflecting students’ problems. We then used the algorithm to train a detector of student problems from about 35,000tweets streamed at the geo-location of Purdue University [2]. This work, for the first time, presents a methodology and results that show how informal social media data can provide insights into students’ experiences [6]. Keywords—Education, computers and education, social networking, web text analysis, twitter, multi label classifiers, Naïve Bayes.