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Using Sociolinguistic Inspired Features for Gender Classification of Web Users

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Publication date2015
Host publicationProceedings of the 18th International Conference of Text, Speech and Dialogue
EditorsP. Kral, V. Matousek
Place of PublicationCham
PublisherSpringer
Pages587-594
Number of pages8
ISBN (electronic)9783319240336
ISBN (print)9783319240329
<mark>Original language</mark>English

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume9302

Abstract

In this article we present a methodology for classification of text from web authors, using sociolinguistic inspired text features. The proposed methodology uses a baseline text mining based feature set, which is combined with text features that quantify results from theoretical and sociolinguistic studies. Two combination approaches were evaluated and the evaluation results indicated a significant improvement in both combination cases. For the best performing combination approach the accuracy was 84.36%, in terms of percentage of correctly classified web posts.