Abstract: In todayís world most of us depend on Social Media to communicate, express our feelings and share information with our friends. Social Media is the medium where now a dayís people feel free to express their emotions. Information from such instrumented environments can present valuable data to report student problem. Examining data from such a social media can be challenging task. The problem of studentís experiences reveal from social media sited need human analysis or Interaction Social Media collects the data in structured and unstructured, formal and informal data as users do not care about the spellings and accurate grammatical construction of a sentence while communicating with each other using different social networking websites ( Face book, Twitter, LinkedIn and YouTube). Gathered data contains sentiments and opinion of users which will be processed using data mining techniques and analyzed for achieving the meaningful information from it. Using Social media data we can classify the type of users by analysis of their posted data on the social web sites. This paper, presents a workflow to integrate both qualitative analysis and large-scale data mining techniques. This study presents a tactic and outcome that demonstrate how casual social media data can present insight into studentís learning experiences.
Keywords: Computers and education, social networking, web text analysis Social media sites and data, Naive Bayes, Support vector machine.