Analyzing Student’s Learning Experiences Through Social Media Data Using Machine Learning Tool
Social media which can be stated as online media supporting social interaction & user contribution is playing a
crucial role in social networking and sharing of data. School, Colleges and universities are beginning to accept social media
as a source of mean for enhancement of education system. Student’s comfortable and accidental talk’s on social media shade
light into their educational experience, mind-set, and worries about their learning procedure. Social media sites such as
twitter, Facebook, and you-tube provides grand platform to large amount of user without any restrictions to share their
opinions, educational learning experience and concerns via their posts Assessment of such data in social network is quite a
challenging process. In the proposed system, there will be advancement to mine the data which constitute both qualitative
analysis and extensive data mining technique. Tweets will be categorized into different categories considering various
striking themes. We use WEKA a data mining/machine learning tool to integrate Naive Bayes classifier and support vector
machine (SVM)on mined data for qualitative analysis purpose to get the deeper understanding of the data and obtain more
accurate results out of the data-set using label based measure to analyze the results. This scheme, presents an approach that
show how unconstrained social media data can provide awareness and insights into students’ learning experiences.
Keywords— Computers and education, Social networking, Social media data, WEKA, Naive Bayes, SVM and Data mining.