ABSTRACT

Social computing permits people to share their opinion or sentiment via social web such as Facebook, Twitter, google+ etc. People always need a suggestion. In past days, people were asked for advice from their family members, colleagues and friends before buying something new, going restaurant for food, visiting somewhere. Now they post a status on Facebook for taking suggestion from their colleagues give them suggestion through comments' then they conclude on the basis of comments.
Now-a-days social sites are also used for the education • purpose to share the information. People spend a lot of their time on Facebook daily, FB is like their regular activity for schools, college or university students as well as for office workers. Social web data is used for the educational data mining.
People express their opinion in a complex way, their status or comments on social sites are truly valuable which are actually seen as their opinion. In this research, we have extracted the comments of MS students from Facebook group (Academic group) and performed opinion analysis on students' comment to determine their opinion towards an educational system, which may help to improve the learning environment of an educational system. For this purpose, a design methodology has proposed in this research which facilitates to extract the comments from the Facebook Academic group using the Facebook graph API, preprocess them, extract features from them and classify them into three groups (Positive, Negative and Neutral) using machine learning (Bayesian Network) technique.