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Gender Classification of Web Authors Using Feature Selection and Language Models

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

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Gender Classification of Web Authors Using Feature Selection and Language Models. / Aravantinou, Christina; Simaki, Vasiliki; Mporas, Iosif et al.
SPECOM 2015: Speech and Computer. ed. / A. Ronzhin; R. Potapova; N. Fakotakis. Cham: Springer, 2015. p. 226-233 (Lecture Notes in Computer Science; Vol. 9319).

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

Harvard

Aravantinou, C, Simaki, V, Mporas, I & Megalooikonomou, V 2015, Gender Classification of Web Authors Using Feature Selection and Language Models. in A Ronzhin, R Potapova & N Fakotakis (eds), SPECOM 2015: Speech and Computer. Lecture Notes in Computer Science, vol. 9319, Springer, Cham, pp. 226-233. https://doi.org/10.1007/978-3-319-23132-7_28

APA

Aravantinou, C., Simaki, V., Mporas, I., & Megalooikonomou, V. (2015). Gender Classification of Web Authors Using Feature Selection and Language Models. In A. Ronzhin, R. Potapova, & N. Fakotakis (Eds.), SPECOM 2015: Speech and Computer (pp. 226-233). (Lecture Notes in Computer Science; Vol. 9319). Springer. https://doi.org/10.1007/978-3-319-23132-7_28

Vancouver

Aravantinou C, Simaki V, Mporas I, Megalooikonomou V. Gender Classification of Web Authors Using Feature Selection and Language Models. In Ronzhin A, Potapova R, Fakotakis N, editors, SPECOM 2015: Speech and Computer. Cham: Springer. 2015. p. 226-233. (Lecture Notes in Computer Science). Epub 2015 Sept 4. doi: 10.1007/978-3-319-23132-7_28

Author

Aravantinou, Christina ; Simaki, Vasiliki ; Mporas, Iosif et al. / Gender Classification of Web Authors Using Feature Selection and Language Models. SPECOM 2015: Speech and Computer. editor / A. Ronzhin ; R. Potapova ; N. Fakotakis. Cham : Springer, 2015. pp. 226-233 (Lecture Notes in Computer Science).

Bibtex

@inbook{7a3fb9c6ad2242179d735568ee586eb3,
title = "Gender Classification of Web Authors Using Feature Selection and Language Models",
abstract = "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.",
keywords = "Text classification , Gender identification , Feature selection ",
author = "Christina Aravantinou and Vasiliki Simaki and Iosif Mporas and Vasileios Megalooikonomou",
year = "2015",
doi = "10.1007/978-3-319-23132-7_28",
language = "English",
isbn = "9783319231310",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "226--233",
editor = "A. Ronzhin and Potapova, {R. } and N. Fakotakis",
booktitle = "SPECOM 2015",

}

RIS

TY - CHAP

T1 - Gender Classification of Web Authors Using Feature Selection and Language Models

AU - Aravantinou, Christina

AU - Simaki, Vasiliki

AU - Mporas, Iosif

AU - Megalooikonomou, Vasileios

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - Text classification

KW - Gender identification

KW - Feature selection

U2 - 10.1007/978-3-319-23132-7_28

DO - 10.1007/978-3-319-23132-7_28

M3 - Chapter (peer-reviewed)

SN - 9783319231310

T3 - Lecture Notes in Computer Science

SP - 226

EP - 233

BT - SPECOM 2015

A2 - Ronzhin, A.

A2 - Potapova, R.

A2 - Fakotakis, N.

PB - Springer

CY - Cham

ER -