Text2Gender: A Deep Learning Architecture for Analysis of Blogger's Age
  and Gender
Vishesh Thakur Aneesh Tickoo
Deep learning techniques have gained a lot of traction in the field of NLP
research. The aim of this paper is to predict the age and gender of an
individual by inspecting their written text. We propose a supervised BERT-based
classification technique in order to predict the age and gender of bloggers.
The dataset used contains 681284 rows of data, with the information of the
blogger's age, gender, and text of the blog written by them. We compare our
algorithm to previous works in the same domain and achieve a better accuracy
and F1 score. The accuracy reported for the prediction of age group was 84.2%,
while the accuracy for the prediction of gender was 86.32%. This study relies
on the raw capabilities of BERT to predict the classes of textual data
efficiently. This paper shows promising capability in predicting the
demographics of the author with high accuracy and can have wide applicability
across multiple domains.