A Transfer Learning Approach to Cross-Domain Author Profiling

Author profiling is the process of analysing text to determine one or more identifying characteristics of the author, mostly used to determine key demographic information. This type of classification problem is ideally suited to machine learning approaches. In this study, a new transfer learning approach is introduced using a pretrained XLNet language model which was then fine-tuned to the specific author profiling task. Informed by previous literature, a Support Vector Machine, Feed-Forward Neural Network, and Convolution Neural Network were also developed for comparison. These algorithms were used to predict gender and age group on a single training and testing domain. As a model that works across multiple domains is desirable, each model was also tested on two domains which were independent of the training domain. The results demonstrated that the transfer learning model is superior to the other methods used for comparison in this study. Although applying the transfer learning model to the cross-domain context decreased its performance, it was still able to achieve a higher degree of accuracy on one testing domain than the Support Vector Machine which was trained and tested on that same domain. In addition, some interesting results emerged regarding the transfer of hyperparameter performance between tasks that share a common factor, be that classification task or training domain.