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Using Sociolinguistic Inspired Features for Gender Classification of Web Users

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Using Sociolinguistic Inspired Features for Gender Classification of Web Users. / Simaki, Vasiliki; Aravantinou, Christina; Mporas, Iosif et al.
Proceedings of the 18th International Conference of Text, Speech and Dialogue. ed. / P. Kral; V. Matousek. Cham: Springer, 2015. p. 587-594 (Lecture Notes in Computer Science; Vol. 9302).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Simaki, V, Aravantinou, C, Mporas, I & Megalooikonomou, V 2015, Using Sociolinguistic Inspired Features for Gender Classification of Web Users. in P Kral & V Matousek (eds), Proceedings of the 18th International Conference of Text, Speech and Dialogue. Lecture Notes in Computer Science, vol. 9302, Springer, Cham, pp. 587-594. https://doi.org/10.1007/978-3-319-24033-6_66

APA

Simaki, V., Aravantinou, C., Mporas, I., & Megalooikonomou, V. (2015). Using Sociolinguistic Inspired Features for Gender Classification of Web Users. In P. Kral, & V. Matousek (Eds.), Proceedings of the 18th International Conference of Text, Speech and Dialogue (pp. 587-594). (Lecture Notes in Computer Science; Vol. 9302). Springer. https://doi.org/10.1007/978-3-319-24033-6_66

Vancouver

Simaki V, Aravantinou C, Mporas I, Megalooikonomou V. Using Sociolinguistic Inspired Features for Gender Classification of Web Users. In Kral P, Matousek V, editors, Proceedings of the 18th International Conference of Text, Speech and Dialogue. Cham: Springer. 2015. p. 587-594. (Lecture Notes in Computer Science). Epub 2015 Dec 11. doi: 10.1007/978-3-319-24033-6_66

Author

Simaki, Vasiliki ; Aravantinou, Christina ; Mporas, Iosif et al. / Using Sociolinguistic Inspired Features for Gender Classification of Web Users. Proceedings of the 18th International Conference of Text, Speech and Dialogue. editor / P. Kral ; V. Matousek. Cham : Springer, 2015. pp. 587-594 (Lecture Notes in Computer Science).

Bibtex

@inproceedings{71660baba7b148d6a32b5b4d4c9f8e67,
title = "Using Sociolinguistic Inspired Features for Gender Classification of Web Users",
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.",
keywords = "text classification algorithms, sociolinguistics, gender identification ",
author = "Vasiliki Simaki and Christina Aravantinou and Iosif Mporas and Vasileios Megalooikonomou",
year = "2015",
doi = "10.1007/978-3-319-24033-6_66",
language = "English",
isbn = "9783319240329",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "587--594",
editor = "P. Kral and V. Matousek",
booktitle = "Proceedings of the 18th International Conference of Text, Speech and Dialogue",

}

RIS

TY - GEN

T1 - Using Sociolinguistic Inspired Features for Gender Classification of Web Users

AU - Simaki, Vasiliki

AU - Aravantinou, Christina

AU - Mporas, Iosif

AU - Megalooikonomou, Vasileios

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - text classification algorithms

KW - sociolinguistics

KW - gender identification

U2 - 10.1007/978-3-319-24033-6_66

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

M3 - Conference contribution/Paper

SN - 9783319240329

T3 - Lecture Notes in Computer Science

SP - 587

EP - 594

BT - Proceedings of the 18th International Conference of Text, Speech and Dialogue

A2 - Kral, P.

A2 - Matousek, V.

PB - Springer

CY - Cham

ER -