International Journal of Engineering and Technology

On different social media sites, students discuss and contribute to their daily encounters in a casual and informal manner. Analyzing such data, though, can be difficult. The complication of students’ experiences reflected from social media content requires human understanding problem of a student’s experience exposes from social media sitedrequirehuman investigation or communication.Examining data from such a social media can be demanding task. In this article, we develop a workflow to combine both qualitative analysis and large-scale data mining techniques. It pays a concentration on engineering student’s Twitter posts to recognize the problem and the difficulty in their educational practices. Based on this result, a multi-label classification algorithm that is Naive Bayes Multi-label Classifier algorithm and Memetic classifier is applied to categorize tweets presenting students' problems.Memetic classifier is a population based approach to split individuals education for problem search, which has had their own advantages in solving optimization problems.