A semantic approach to classifying Twitter users

Abstract

Social media has grown rapidly in the past several years. Twitter in particular has seen a significant rise in its user audience because of the short and compact Tweet concept (140 characters). As more users come on board, it provides a large market for companies to advertise and find prospective customers by classifying users into different market categories. Traditional classification methods use TF�IDF and bag of words concept as the feature vector which inevitably is of large dimensions. In this paper we propose a method to improve the method of classification using semantic information to reduce dimensions of the feature vectors and validate this method by feeding them into multiple learning algorithms and evaluating the results. � Springer Nature Singapore Pte Ltd. 2018.