Analyse and Classify Rural Area Engineering Students Twitter Posts using Data Mining Technique

Students informal conversations on social media such as Twitter and Whatsapp, 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 to automatic data analysis techniques for classify twitter data. Proposed new system is 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 conduct a qualitative analysis using ML studio on tweets collected from engineering colleges using term #DStudents problems, engineering Problem, Aluminisuggestions and lady Engineer. Collected tweets are related to engineering students’ college life. In proposed system 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.