Personality Analysis for Social Media Users using Arabic language and its Effect on Sentiment Analysis

TL;DR

This study investigates how Arabic language use on Twitter relates to personality traits and influences sentiment analysis, using machine learning models trained on a dataset of Arabic-speaking users who took personality tests.

Contribution

It introduces the AraPers dataset, develops a BERT-based model for personality prediction, and demonstrates the impact of personality on sentiment analysis in Arabic social media content.

Findings

Achieved 74.86% accuracy in personality trait classification

Linguistic and profile features effectively differentiate personality traits

Personality traits significantly influence sentiment analysis outcomes

Abstract

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.