Personality Classification Model of Social Network Profiles based on their Activities and Contents
Research Abstract
Abstract—Social networks have become an important part of
everyday life, especially after the latest technologies such as
smartphones, tablets, and laptops have become widespread.
Individuals spend a lot of time on social media and express their
feelings and opinions through statuses, comments, and updates
which could be a way to understand and classify their
personalities. The personalities in psychological science are
divided into five classes according to the Big-five model
(Openness, Extraversion, Consciousness, Agreeableness, and
Neurotic). This model shows the key features with their weights
for each personality. In this paper, a proposed model is
developed for detecting the personality features from users’
activities in social networks. In this model, machine learning
techniques are used for predicting the personalities with a score
for each Big-five model type and sorting them in descending
order. The personality classification model will be useful in
developing a better understanding of the user profile and
specifically targeting users with appropriate advertising. Any
social media network user's personality can be predicted by
using their posts and status updates to get better accuracy. The
experimental results of the model in this study provide an
enhancement because it can predict the precise score of one user
in each factor of the Big-five. The proposed model was tested on
a dataset extracted from Facebook and manually classified by
experts, and it achieved 89.37% accuracy.
Research Keywords
—Psychological personality; machine learning techniques; big-five; LinearSVC