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Gender Classification of Web Authors Using Feature Selection and Language Models

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)peer-review

Publication date2015
Host publicationSPECOM 2015: Speech and Computer
EditorsA. Ronzhin, R. Potapova, N. Fakotakis
Place of PublicationCham
Number of pages8
ISBN (Electronic)9783319231327
ISBN (Print)9783319231310
<mark>Original language</mark>English

Publication series

NameLecture Notes in Computer Science


In the present article, we address the problem of automatic gender classification of web blog authors. More specifically, we employ eight widely used machine learning algorithms, in order to study the effectiveness of feature selection on improving the accuracy of gender classification. The feature ranking is performed over a set of statistical, part-of-speech tagging and language model features. In the experiments, we employed classification models based on decision trees, support vector machines and lazy-learning algorithms. The experimental evaluation performed on blog author gender classification data demonstrated the importance of language model features for this task and that feature selection significantly improves the accuracy of gender classification, regardless of the type of the machine learning algorithm used.