Inferring Personality From Social Media User Behaviors Using Dense Net Convolutional Neural Networks

Abstract

We live in a world where social media is omnipresent and integrated into our daily lives. People love to express their interests, thoughts, and opinions on these social networking platforms. This information reveals several psychological aspects of their behavior and can be used to predict their personality. To predict this, introduce the method dense net convolutional neural network (DNCNN) is based on predicting the social media users' personality identification. Performed an experimental evaluation on a benchmark dataset for the task of categorizing personality traits into distinct classifications. The review of the dataset yields improved results, showing that the proposed model can really arrange client character attributes when contrasted with cutting-edge models. Posts and status updates can be used to predict the personality of users of social media networks to improve accuracy. These results show that picture features are better predictors of personality than text features, and also found that a profile picture reliably predicts personality with 96% accuracy.