Abstract
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.
Highlights
The availability of large amounts of texts obtained through the Internet and the anonymity of the texts have revealed the potential of authorship analysis, which deals with the task of authorship attribution where knowing the author of a document, or with the task of authorship profiling, where the authors’ personality type, age, and gender are determined
We evaluated traditional machine learning (ML) and neural networks architectures to address the problem of author gender identification for Turkish
A list of ML classifiers was trained using several vector representations obtained by the variants of BoW and distributed vector representations such as Word2vec, GloVe, and Doc2vec
Summary
The availability of large amounts of texts obtained through the Internet and the anonymity of the texts have revealed the potential of authorship analysis, which deals with the task of authorship attribution where knowing the author of a document, or with the task of authorship profiling, where the authors’ personality type, age, and gender are determined. Author gender identification, which is a subproblem of the authorship profiling problem, aims at determining the gender of an author of a given text. Author gender identification has gained importance in various commercial applications including e-mail forgery, online communities, security, forensics, trading, and marketing. People avoid providing their real identity information on social media platforms, which leads to an anonymity problem. The question “Can we identify author gender for a given text?” is very relevant for practical applications
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