Simaki, Vasiliki and Mporas, Iosif and Megalooikonomou, Vasileios (2016) Age Identification of Twitter Users : Classification Methods and Sociolinguistic Analysis. In: Computational Linguistics and Intelligent Text Processing : 17th International Conference, CICLing 2016, Konya, Turkey, April 3–9, 2016, Revised Selected Papers, Part II. Lecture Notes in Computer Science . Springer, Cham, pp. 385-395. ISBN 9783319754864
Full text not available from this repository.Abstract
In this article, we address the problem of age identification of Twitter users, after their online text. We used a set of text mining, sociolinguistic-based and content-related text features, and we evaluated a number of well-known and widely used machine learning algorithms for classification, in order to examine their appropriateness on this task. The experimental results showed that Random Forest algorithm offered superior performance achieving accuracy equal to 61%. We ranked the classification features after their informativity, using the ReliefF algorithm, and we analyzed the results in terms of the sociolinguistic principles on age linguistic variation.