Prediction of Author's Profile Basing on Fine-Tuning BERT Model

The task of author profiling consists in specifying the infer-demographic features of the social networks' users by studying their published content or the interactions between them. In the literature, many research works were conducted to enhance the accuracy of the techniques used in this process. In fact, the existing methods can be divided into two types: simple linear models and complex deep neural network models. Among them, the transformer-based model exhibited the highest efficiency in NLP analysis in several languages (English, German, French, Turk, Arabic, etc.). Despite their good performance, these approaches do not cover author profiling analysis and, thus, should be further enhanced. So, we propose in this paper a new deep learning strategy by training a customized transformer-model to learn the optimal features of our dataset. In this direction, we fine-tune the model by using the transfer learning approach to improve the results with random initialization. We have achieved about 79% of accuracy by modifying model to apply the retraining process using PAN 2018 authorship dataset. © 2024 Slovene Society Informatika. All rights reserved.