Personality Analysis for Social Media Users using Arabic language and its Effect on Sentiment Analysis
<|reference_start|>Personality Analysis for Social Media Users using Arabic language and its Effect on Sentiment Analysis: 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.<|reference_end|>
@article{dandash2024personality,
    title={Personality Analysis for Social Media Users using Arabic language and
  its Effect on Sentiment Analysis},
    author={Mokhaiber Dandash, Masoud Asadpour},
    journal={arXiv preprint arXiv:2407.06314},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06314},
    primaryClass={cs.CL}
}