This paper presents a computational approach to author profiling taking gender and language variety into account. We apply an ensemble system with the output of multiple linear SVM classifiers trained on character and word n-grams. We evaluate the system using the dataset provided by the organizers of the 2017 PAN lab on author profiling. Our approach achieved 75% average accuracy on gender identification on tweets written in four languages and 97% accuracy on language variety identification for Portuguese.
@article{arxiv.1707.00621,
title = {Including Dialects and Language Varieties in Author Profiling},
author = {Alina Maria Ciobanu and Marcos Zampieri and Shervin Malmasi and Liviu P. Dinu},
journal= {arXiv preprint arXiv:1707.00621},
year = {2017}
}