Using sociolinguistic inspired features for gender classification of web authors

Vasiliki Simaki; Christina Aravantinou; Iosif Mporas; Vasileios Megalooikonomou

doi:10.1007/978-3-319-24033-6_66

Using sociolinguistic inspired features for gender classification of web authors

Vasiliki Simaki, Christina Aravantinou, Iosif Mporas, Vasileios Megalooikonomou

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

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.

Original language	English
Title of host publication	Text, Speech, and Dialogue - 18th International Conference, TSD 2015, Proceedings
Editors	Václav Matoušek, Pavel Král
Publisher	Springer Nature
Pages	587-594
Number of pages	8
ISBN (Print)	9783319240329
DOIs	https://doi.org/10.1007/978-3-319-24033-6_66
Publication status	Published - 1 Jan 2015
Externally published	Yes
Event	18th International Conference on Text, Speech and Dialogue, TSD 2015 - Pilsen, Czech Republic Duration: 14 Sept 2015 → 17 Sept 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9302
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Text, Speech and Dialogue, TSD 2015
Country/Territory	Czech Republic
City	Pilsen
Period	14/09/15 → 17/09/15

Keywords

Gender identification
Sociolinguistics
Text classification algorithms

Access to Document

10.1007/978-3-319-24033-6_66

Cite this

Simaki, V., Aravantinou, C., Mporas, I., & Megalooikonomou, V. (2015). Using sociolinguistic inspired features for gender classification of web authors. In V. Matoušek, & P. Král (Eds.), Text, Speech, and Dialogue - 18th International Conference, TSD 2015, Proceedings (pp. 587-594). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9302). Springer Nature. https://doi.org/10.1007/978-3-319-24033-6_66

Simaki, Vasiliki ; Aravantinou, Christina ; Mporas, Iosif et al. / Using sociolinguistic inspired features for gender classification of web authors. Text, Speech, and Dialogue - 18th International Conference, TSD 2015, Proceedings. editor / Václav Matoušek ; Pavel Král. Springer Nature, 2015. pp. 587-594 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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author = "Vasiliki Simaki and Christina Aravantinou and Iosif Mporas and Vasileios Megalooikonomou",

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booktitle = "Text, Speech, and Dialogue - 18th International Conference, TSD 2015, Proceedings",

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Simaki, V, Aravantinou, C, Mporas, I & Megalooikonomou, V 2015, Using sociolinguistic inspired features for gender classification of web authors. in V Matoušek & P Král (eds), Text, Speech, and Dialogue - 18th International Conference, TSD 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9302, Springer Nature, pp. 587-594, 18th International Conference on Text, Speech and Dialogue, TSD 2015, Pilsen, Czech Republic, 14/09/15. https://doi.org/10.1007/978-3-319-24033-6_66

Using sociolinguistic inspired features for gender classification of web authors. / Simaki, Vasiliki; Aravantinou, Christina; Mporas, Iosif et al.
Text, Speech, and Dialogue - 18th International Conference, TSD 2015, Proceedings. ed. / Václav Matoušek; Pavel Král. Springer Nature, 2015. p. 587-594 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9302).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Simaki V, Aravantinou C, Mporas I, Megalooikonomou V. Using sociolinguistic inspired features for gender classification of web authors. In Matoušek V, Král P, editors, Text, Speech, and Dialogue - 18th International Conference, TSD 2015, Proceedings. Springer Nature. 2015. p. 587-594. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-24033-6_66

Using sociolinguistic inspired features for gender classification of web authors

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