|Project Description: ||Recently, social media is playing a vital role in social networking and sharing
of data. Social media is favored by many users as it is available to millions of people
without any limitations to share their opinions, educational learning experience and
concerns via their status. Twitter API, twitter4j, is processed to search for the tweets
based on the geo location.
Student’s posts on social network gives us a better concern to take decision about
the particular education system’s learning process of the system. Evaluating such data in
social network is quite a challenging process. In the proposed system, there will be a
workflow to mine the data which integrates both qualitative analysis and large scale data
mining technique. Based on the different prominent themes tweets will be categorized
into different groups.
Naïve Bayes classifier will be implemented on mined data for qualitative analysis
purpose to get the deeper understanding of the data. It uses multi label classification
technique as each label falls into different categories and all the attributes are
independent to each other. Label based measures will be taken to analyze the results and
comparing them with the existing sentiment analysis technique.