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Students’ informal conversations on Twitter useful for know the student educational experiences concerns about the learning process. Data from Twitter and other social networking environments can provide valuable knowledge to inform student learning. However, the growing large scale of data required automatic data analysis and mining techniques. Proposed new system which will be help to developing a workflow to integrate both qualitative analysis and large-scale data mining techniques. This will be focusing on engineering students’ Twitter posts to understand issues and problems in their educational life. First conduct a qualitative analysis on samples taken from tweets related to engineering students’ college life. In proposed system will used a multi-label classification algorithm to classify tweets reflecting students’ problems such as heavy study load, lack of motivation, stress, lack of social engagement, sleep deprivation and others.

Analyze engineering student’s twitter posts to understand issues and problems in their educational experiences