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Gender Classification of Web Authors Using Feature Selection and Language Models

Aravantinou, Christina; Simaki, Vasiliki LU ; Mporas, Iosif and Megalooikonomou, Vasileios (2015) In Lecture Notes in Computer Science 9319. p.226-233
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Text classification , Gender identification, Feature selection
in
Lecture Notes in Computer Science
editor
Ronzhin, Andrey; Potapova, Rodmonga; Fakotakis, Nikos; ; and
volume
9319
pages
226 - 233
publisher
Springer
external identifiers
  • scopus:84945952760
ISSN
0302-9743
ISBN
978-3-319-23131-0
978-3-319-23132-7
DOI
10.1007/978-3-319-23132-7_28
language
English
LU publication?
no
id
e4a3881a-0566-4dfa-a411-19ff9c0f0102
date added to LUP
2017-06-02 19:08:22
date last changed
2017-10-22 05:31:21
@inbook{e4a3881a-0566-4dfa-a411-19ff9c0f0102,
  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.},
  author       = {Aravantinou, Christina and Simaki, Vasiliki and Mporas, Iosif and Megalooikonomou, Vasileios},
  editor       = {Ronzhin, Andrey and Potapova, Rodmonga and Fakotakis, Nikos},
  isbn         = {978-3-319-23131-0},
  issn         = {0302-9743},
  keyword      = {Text classification ,Gender identification,Feature selection},
  language     = {eng},
  pages        = {226--233},
  publisher    = {Springer},
  series       = {Lecture Notes in Computer Science},
  title        = {Gender Classification of Web Authors Using Feature Selection and Language Models},
  url          = {http://dx.doi.org/10.1007/978-3-319-23132-7_28},
  volume       = {9319},
  year         = {2015},
}