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/).
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 -