Social media is heading toward personalization more and more, 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 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.

研究探索推特上阿拉伯语使用、个性特质以及其对情感分析的影响，使用语言特征、个人资料统计和其他特征如表情符号进行分析，发现语言特征、个人资料特征和相关模型可以区分不同的个性特质，并且个性特质会影响社交媒体中的情感；对社交媒体上的人类行为与个性特征的关系有助于政治话语分析和舆论追踪等实际应用。

使用阿拉伯语进行社交媒体用户的个性分析及其对情感分析的影响