Efficient user profiling in twitter social network using traditional classifiers

Abstract

Any discussion in social media can be fruitful if the people involved in the discussion are related to a field. In a similar way to advertise an event, it is useful to find users who are interested in the content of the event. In social networks like Twitter, which contain a large number of users, the categorization of users based on their interests will help this cause. This paper presents an efficient supervised machine learning approach which categorizes Twitter users based on three important features(Tweet-based, User-based and Time-series based) into six interest categories - Politics, Entertainment, Entrepreneurship, Journalism, Science & Technology and Healthcare. We compare the proposed feature set with different traditional classifiers like Support Vector Machines, Naive-Bayes, k-Nearest Neighbours, Decision Tree and Logistic Regression, and obtain upto 89.82% accuracy in classification. We also propose a design for a real-time system for Twitter user profiling along with a prototype implementation. © Springer International Publishing Switzerland 2016.