Journal of Robotics, Networking and Artificial Life

Volume 3, Issue 1, June 2016, Pages 24 - 27

Estimating Age on Twitter Using Self-Training Semi-Supervised SVM

Authors
Tatsuyuki Iju, Satoshi Endo, Koji Yamada, Naruaki Toma, Yuhei Akamine
Corresponding Author
Tatsuyuki Iju
Available Online 1 June 2016.
DOI
10.2991/jrnal.2016.3.1.6How to use a DOI?
Keywords
Twitter, Age, Semi-supervised learning, Self-training, SVM, Plat scaling
Abstract

The estimation methods for Twitter user’s attributes typically require a vast amount of labeled data. Therefore, an efficient way is to tag the unlabeled data and add it to the set. We applied the self-training SVM as a semi-supervised method for age estimation and introduced Plat scaling as the unlabeled data selection criterion in the self-training process. We show how the performance of the self-training SVM varies when the amount of training data and the selection criterion values are changed.

Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
3 - 1
Pages
24 - 27
Publication Date
2016/06/01
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
10.2991/jrnal.2016.3.1.6How to use a DOI?
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Tatsuyuki Iju
AU  - Satoshi Endo
AU  - Koji Yamada
AU  - Naruaki Toma
AU  - Yuhei Akamine
PY  - 2016
DA  - 2016/06/01
TI  - Estimating Age on Twitter Using Self-Training Semi-Supervised SVM
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 24
EP  - 27
VL  - 3
IS  - 1
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2016.3.1.6
DO  - 10.2991/jrnal.2016.3.1.6
ID  - Iju2016
ER  -