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.
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References
Anand, A., Gorde, K., Moniz, J.R.A., Park, N., Chakraborty, T., Chu, B.T.: Phishing URL detection with oversampling based on text generative adversarial networks. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 1168–1177. IEEE (2018)
Bartoli, A., De Lorenzo, A., Medvet, E., Tarlao, F.: Your paper has been accepted, rejected, or whatever: automatic generation of scientific paper reviews. In: Buccafurri, F., Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-ARES 2016. LNCS, vol. 9817, pp. 19–28. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45507-5_2
Barua, S., Islam, M.M., Murase, K.: ProWSyn: proximity weighted synthetic oversampling technique for imbalanced data set learning. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7819, pp. 317–328. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37456-2_27
Batista, G.E.A.P.A., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor. 6, 20–29 (2004)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Chen, E., Lin, Y., Xiong, H., Luo, Q., Ma, H.: Exploiting probabilistic topic models to improve text categorization under class imbalance. Inf. Process. Manage. 47(2), 202–214 (2011)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: KDD (2016)
Fernández, A.: SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J. Artif. Intell. Res. 61, 863–905 (2018)
Hu, J., Zeng, H.J., Li, H., Niu, C., Chen, Z.: Demographic prediction based on user’s browsing behavior. In: WWW (2007)
Jindal, R., Malhotra, R., Jain, A.: Techniques for text classification: literature review and current trends. Webology 12(2), 1–28 (2015)
Kabbur, S., Han, E.H., Karypis, G.: Content-based methods for predicting web-site demographic attributes. In: 2010 IEEE International Conference on Data Mining, pp. 863–868 (2010)
Kim, D.Y., Lehto, X.Y., Morrison, A.M.: Gender differences in online travel information search: implications for marketing communications on the internet. Tourism Manage. 28(2), 423–433 (2007)
Koto, F.: SMOTE-Out, SMOTE-Cosine, and Selected-SMOTE: an ehancement strategy to handle imbalance in data level. In: 2014 International Conference on Advanced Computer Science and Information System, pp. 280–284 (2014)
Lee, S.K., Hong, S.J., Yang, S.I.: Oversampling for imbalanced data classification using adversarial network. In: 2018 International Conference on Information and Communication Technology Convergence (ICTC), pp. 1255–1257. IEEE (2018)
McMahan, C., Hovland, R., McMillan, S.: Online marketing communications: exploring online consumer behavior by examining gender differences and interactivity within internet advertising. J. Interact. Advertising 10(1), 61–76 (2009)
Nigam, A., Johnson, R.A., Wang, D., Chawla, N.V.: Characterizing online health and wellness information consumption: a study. Inf. Fusion 46, 33–43 (2019)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Phuong, T.M., et al.: Gender prediction using browsing history. In: Huynh, V., Denoeux, T., Tran, D., Le, A., Pham, S. (eds.) Knowledge and Systems Engineering, pp. 271–283. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-02741-8_24
Potash, P., Romanov, A., Rumshisky, A.: Ghostwriter: using an LSTM for automatic rap lyric generation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1919–1924 (2015)
Sun, A., Lim, E.P., Liu, Y.: On strategies for imbalanced text classification using SVM: a comparative study. Decis. Support Syst. 48(1), 191–201 (2009)
Yu, L., Zhang, W., Wang, J., Yu, Y.: SeqGAN: sequence generative adversarial nets with policy gradient. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
Zhu, X., Liu, Y., Qin, Z., Li, J.: Data Augmentation in Emotion Classification Using Generative Adversarial Networks. ArXiv abs/1711.00648 (2017)
Acknowledgements
We thank Trenton Ford for helpful discussions. This work was supported in part by the National Science Foundation (NSF) Grant IIS-1447795.
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Syed, M., Marshall, J., Nigam, A., Chawla, N.V. (2019). Gender Prediction Through Synthetic Resampling of User Profiles Using SeqGANs. In: Tagarelli, A., Tong, H. (eds) Computational Data and Social Networks. CSoNet 2019. Lecture Notes in Computer Science(), vol 11917. Springer, Cham. https://doi.org/10.1007/978-3-030-34980-6_38
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