Mining Twitter For Analysing Students’ Learning Experiences
As social media plays an important role in social networking and sharing data, it is available to millions of users
and they have the provision to share their opinions, experience and concerns through their status. Social media can also helps
in understanding the human behaviour through large data sets. Engineering Students also had a great impact upon these
social medias. Their informal conversations on social media such as twitter had shed light into their educational experiences,
opinions, feelings, and concerns about the learning process and provide knowledge to understand about the student learning
issues. In this paper, a workflow is proposed to integrate both qualitative analysis and large-scale data mining techniques to
effectively analyse such data. Heavy study load, lack of social engagement, and sleep deprivation are some of the problems
found in engineering students. A Decision tree multi-label classification algorithm is being implemented to classify tweets
reflecting students’ problems. This can help to study about these issues and its influence upon social Medias.
Keywords—Social Network, twitter, education, decision tree, multi-label classification.