Using Sociolinguistic Inspired Features for Gender Classification of Web Users

Simaki, Vasiliki and Aravantinou, Christina and Mporas, Iosif and Megalooikonomou, Vasileios (2015) Using Sociolinguistic Inspired Features for Gender Classification of Web Users. In: Proceedings of the 18th International Conference of Text, Speech and Dialogue. Lecture Notes in Computer Science . Springer, Cham, pp. 587-594. ISBN 9783319240329

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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.

Item Type:
Contribution in Book/Report/Proceedings
Subjects:
ID Code:
124803
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Deposited On:
23 Apr 2018 12:48
Refereed?:
Yes
Published?:
Published
Last Modified:
15 Jan 2020 06:58