  Social media is heading towards more and more personalization, where
individuals reveal their beliefs, interests, habits, and activities, simply
offering glimpses into their personality traits. This study, explores the
correlation between the use of Arabic language on twitter, personality traits
and its impact on sentiment analysis. We indicated the personality traits of
users based on the information extracted from their profile activities, and the
content of their tweets. Our analysis incorporated linguistic features, profile
statistics (including gender, age, bio, etc.), as well as additional features
like emoticons. To obtain personality data, we crawled the timelines and
profiles of users who took the 16personalities test in Arabic on
16personalities.com. Our dataset, "AraPers", comprised 3,250 users who shared
their personality results on twitter. We implemented various machine learning
techniques, to reveal personality traits and developed a dedicated model for
this purpose, achieving a 74.86% accuracy rate with BERT, analysis of this
dataset proved that linguistic features, profile features and derived model can
be used to differentiate between different personality traits. Furthermore, our
findings demonstrated that personality affect sentiment in social media. This
research contributes to the ongoing efforts in developing robust understanding
of the relation between human behaviour on social media and personality
features for real-world applications, such as political discourse analysis, and
public opinion tracking.
