Gender Prediction Through Synthetic Resampling of User Profiles Using SeqGANs

Munira Syed

Abstract:

. Generative Adversarial Networks (GANs) have enabled
researchers to achieve groundbreaking results on generating synthetic
images. While GANs have been heavily used for generating synthetic
image data, there is limited work on using GANs for synthetically resampling the minority class, particularly for text data. In this paper, we
utilize Sequential Generative Adversarial Networks (SeqGAN) for creating synthetic user profiles from text data. The text data consists of
articles that the users have read that are representative of the minority
class. Our goal is to improve the predictive power of supervised learning
algorithms for the gender prediction problem, using articles consumed
by the user from a large health-based website as our data source. Our
study shows that by creating synthetic user profiles for the minority
class with SeqGANs and passing in the resampled training data to an
XGBoost classifier, we achieve a gain of 2% in AUROC, as well as a 3%
gain in both F1-Score and AUPR for gender prediction when compared
to SMOTE. This is promising for the use of GANs in the application of
text resampling.