An Emotion-aided Gender Prediction System
Chanchal Suman, Rohit Chaudhari, Sriparna Saha, Sudhir Kumar, Pushpak Bhattacharyya · 2021
The gender of a user plays a very important role in the development of the personalized online services. However, due to privacy, and anonymity, gender information is usually not available for many users. Since male and female users have differences in their message contents, the messages posted by users can be analyzed for finding their genders. Users with different genders express their emotions differently too. In the current paper, we introduce an emotion-aided gender prediction system. The intuition behind our approach is to predict the gender of a user based on emotional clues. The proposed approach consists of two neural network branches, one for gender and the other for emotion. Both networks share their bottom feature extraction module and are optimized within a multi-task learning framework. Gender and emotion networks are trained over our own annotated dataset having gender and emotion labels. Experimental results show the effectiveness of using emotion for predicting the gender of a user.