Basic Informations
C.V
2023 Department of Information Systems - College of Computing and Artificial Intelligence. Helwan University
Master's Degree in Information Systems - “Personality classification model for social network profiles based on their activities and contents”
2012-2016 - Department of Information Systems - College of Computing and Artificial Intelligence. Beni Suef University
Bachelor's. Information systems with a general rating of excellent
Master Title
Psychological Personality Classification on User Generated Content by Using Machine Learning Techniques
Master Abstract
Social networks have become a vital part of everyday life,
particularly after the latest technologies such as tablets,
smartphones, and laptops have become widespread. Individuals
spend a lot of time on social media and express their feelings and
opinions through status updates, comments, and updates, which
could be a way to understand and classify their personalities. The
personalities in psychological science are categorized into five
classes according to the Big-5 model (Openness, Extraversion,
Consciousness, Agreeableness, and Neurotic). This model
demonstrates the key features with their weights for each
personality. In this thesis, a proposed model is developed for
detecting personality features from users’ activities in social
networks. In this model, machine learning techniques are used for
predicting the personalities by assigning a score to each Big-five
trait 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. In this study, the
proposed model is based on the LinearSVC and was tested on a
dataset extracted from Facebook and manually classified posts into
31 classes by experts, and it achieved 92.52% accuracy.
PHD Title
Not found
PHD Abstract
Not found