Volume 7 Issue 3 - September 2016

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

    Authors : S.d.rane, Prof.u.a.nuli, Prof.n.s.mahajan

    Pages : 392-395

    DOI : http://dx.doi.org/10.21172/1.73.551

    Keywords : Data mining, Text classification, Tweets, Social networking.

    Abstract :

    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.

    Citing this Journal Article :

    S.d.rane, Prof.u.a.nuli, Prof.n.s.mahajan, "Analyze engineering student’s twitter posts to understand issues and problems in their educational experiences ", https://www.ijltet.org/journal_details.php?id=907&j_id=3363, Volume 7 Issue 3 - September 2016, 392-395, #ijltetorg