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Evaluation and sociolinguistic analysis of text features for gender and age identification

Simaki, Vasiliki LU ; Mporas, Iosif and Megalooikonomou, Vasileios (2016) In American Journal of Engineering and Applied Sciences 9(4). p.868-876
Abstract

The paper presents an interdisciplinary study in the field of automatic gender and age identification, under the scope of sociolinguistic knowledge on gendered and age linguistic choices that social media users make. The authors investigated and gathered standard and novel text features used in text mining approaches on the author’s demographic information and profiling and they examined their efficacy in gender and age detection tasks on a corpus consisted of social media texts. An analysis of the most informative features is attempted according to the nature of each feature and the information derived after the characteristics’ score of importance is discussed.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Age identification, Feature ranking, Gender detection, ReliefF algorithm, Sociolinguistics, Text mining
in
American Journal of Engineering and Applied Sciences
volume
9
issue
4
pages
9 pages
publisher
Neuroscience Publications
external identifiers
  • scopus:85008701961
ISSN
1941-7020
DOI
10.3844/ajeassp.2016.868.876
language
English
LU publication?
yes
id
66fedcef-f06a-49f3-a447-38538f9875f2
date added to LUP
2017-02-21 14:33:51
date last changed
2017-10-01 05:30:52
@article{66fedcef-f06a-49f3-a447-38538f9875f2,
  abstract     = {<p>The paper presents an interdisciplinary study in the field of automatic gender and age identification, under the scope of sociolinguistic knowledge on gendered and age linguistic choices that social media users make. The authors investigated and gathered standard and novel text features used in text mining approaches on the author’s demographic information and profiling and they examined their efficacy in gender and age detection tasks on a corpus consisted of social media texts. An analysis of the most informative features is attempted according to the nature of each feature and the information derived after the characteristics’ score of importance is discussed.</p>},
  author       = {Simaki, Vasiliki and Mporas, Iosif and Megalooikonomou, Vasileios},
  issn         = {1941-7020},
  keyword      = {Age identification,Feature ranking,Gender detection,ReliefF algorithm,Sociolinguistics,Text mining},
  language     = {eng},
  number       = {4},
  pages        = {868--876},
  publisher    = {Neuroscience Publications},
  series       = {American Journal of Engineering and Applied Sciences},
  title        = {Evaluation and sociolinguistic analysis of text features for gender and age identification},
  url          = {http://dx.doi.org/10.3844/ajeassp.2016.868.876},
  volume       = {9},
  year         = {2016},
}