Social media allows the creation and interactions of user created content. Social medium places include Facebook, Twitter etc. Student's casual discussion on social media focused into their educational experience, mind-set, and worry about the learning procedure. Information from such uninstrumented environments can present valuable data to report student problem. Examining data from such a social meadiacan be challenging task. The problem of student's experiences reveal from social media sited need human analysis or Interaction. It pays attention on engineering student's Twitter posts to know problem and troubles in their educational practices. This paper proposes a workflow to put together both qualitative investigation and large-scale data mining scheme. First a sample is taken from student and then qualitative analysis conducted on that sample which is associated to engineering student's educational life. So only tweets related to engineering student is collected. It is found that engineering students encounter problems such as heavy learning load, lack of social meeting, and sleep deficiency. Based on this outcome, a multi-label classification algorithm that is Naive Bayes Multi-label Classifier algorithm is applied to categorize tweets presenting student's problems. Then decision tee algorithm is applied to make more accurate result it will perform filtering. The algorithm prepares a detector of student problems. This study presents a tactic and outcome that demonstrate how casual social media data can present insight into student's incident.

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Social Networking Web-text Analysis Education Social Network Analysis Computer And Education Data Mining