Prediction of Author’s Profile basing on Fine-Tuning BERT model
Bassem Bsir, Nabil Khoufi, Mounir Zrigui · Informatica · 2024
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 mod-els and complex deep neural network models. Among them, the transformer-based model exhibited the highest efficiency in NLP analysis in several lan-guages (English, German, French, Turk, Arabic, etc.). Despite their good per-formance, 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 fea-tures of our dataset. In this direction, we fine-tune the model by using the trans-fer learning approach to improve the results with random initialization. We have achieved about 79% of accuracy by modifying model to apply the retrain-ing process using PAN 2018 authorship dataset.