A comparative study of author gender identification

In recent years, author gender identification has gained considerable attention in the fields of information retrieval and computational linguistics. In this paper, we employ and evaluate different learning approaches based on machine learning (ML) and neural network language models to address the problem of author gender identification. First, several ML classifiers are applied to the features obtained by bag-of-words. Secondly, datasets are represented by a low-dimensional real-valued vector using Word2vec, GloVe, and Doc2vec, which are on par with ML classifiers in terms of accuracy. Lastly, neural networks architectures, the convolution neural network and recurrent neural network, are trained and their associated performances are assessed. A variety of experiments are successfully conducted. Different issues, such as the effects of the number of dimensions, training architecture type, and corpus size, are considered. The main contribution of the study is to identify author gender by applying word embeddings and deep learning architectures to the Turkish language.