Mining Twitter Data of Rural Area Engineering Colleges for Understanding Issues and Problems in Their Educational Experiences
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2017, Vol 5, Issue 5
Abstract
Students’ informal conversations on social media such as Twitter are useful for understand their learning experiences, and feelings. Data from such social media environments can provide valuable information about students learning system. Collecting and analyzing data from such media can be difficult task. However, the large scale of data required automatic data analysis techniques for classify twitter data . We developed new system to combination of qualitative analysis and large-scale data mining techniques. This system focuses on engineering students’ Twitter posts which are collected from rural area engineering colleges to understand issues and problems in their learning. First we conduct a qualitative analysis on tweets collected from engineering colleges using term #DStudentsproblems. Collected tweets are related to engineering students’ college life. In proposed system we used a multi-label classification algorithm to classify tweets reflecting students’ problems such as soft skill issues, heavy study load, lack of social engagement, and sleep problems.
Authors and Affiliations
S. D. Rane, U. A. Nuli, N. S. Mahajan
Keywords
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