A Cascade Approach for Gender Prediction from Texts in Portuguese Language

João Pedro Moreira de Morais, L. Merschmann
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Abstract

Author Profiling is a prominent research area in which computational approaches have been proposed to predict authors’ characteristics from their texts. Gender, age, personality traits, and occupation are examples of commonly analyzed characteristics. It is a task of growing importance, with applications in different areas such as forensics, marketing, and e-commerce. Although a lot of research has been conducted on this task for some widely used languages (e.g., English), there is still a lot of room for improvement in studies involving the Portuguese language. Thus, this work contributes by proposing and evaluating a cascading approach, which combines a weighted lexical approach, a heuristic, and a classifier, for the gender prediction problem using only textual content written in the Portuguese language. The proposed approach considers both specificities of the Portuguese language and domain characteristics of the texts. The results obtained from the proposed approach showed that exploring the specificities of the Portuguese language and domain characteristics of the texts can positively contribute to the performance of the gender prediction task.
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葡萄牙语文本性别预测的级联方法
作者特征分析是一个突出的研究领域,已经提出了计算方法来预测作者的文本特征。性别、年龄、性格特征和职业是通常分析的特征。这是一项越来越重要的任务,在取证、市场营销和电子商务等不同领域都有应用。尽管对于一些广泛使用的语言(如英语)已经进行了大量的研究,但在涉及葡萄牙语的研究中仍有很大的改进空间。因此,这项工作提出并评估了一种级联方法,该方法结合了加权词汇方法、启发式方法和分类器,用于仅使用葡萄牙语编写的文本内容的性别预测问题。提出的方法考虑了葡萄牙语的特殊性和文本的领域特征。该方法的结果表明,探索葡萄牙语的特殊性和文本的领域特征对性别预测任务的执行有积极的贡献。
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