使用阿拉伯语的社交媒体用户的人格分析及其对情感分析的影响
Personality Analysis for Social Media Users using Arabic language and its Effect on Sentiment Analysis
AI总结：
本研究构建了包含3250名用户的阿拉伯语Twitter人格数据集AraPers，利用BERT等机器学习技术结合语言与个人资料特征预测人格特质（准确率74.86%），并证实人格特质对社交媒体情感分析有显著影响。
摘要：
社交媒体正朝着越来越个性化的方向发展，个体在其中展示他们的信念、兴趣、习惯和活动，简单地展现了他们的人格特质。本研究探讨了在Twitter上使用阿拉伯语、人格特质及其对情感分析影响之间的相关性。我们根据从用户的个人资料活动及其推文内容中提取的信息，指出了用户的人格特质。我们的分析结合了语言特征、个人资料统计数据（包括性别、年龄、个人简介等），以及表情符号等附加特征。为了获取人格数据，我们抓取了在16personalities.com上参加阿拉伯语16personalities测试的用户的时间线和个人资料。我们的数据集“AraPers”包含3,250名在Twitter上分享其人格测试结果的用户。我们实施了各种机器学习技术来揭示人格特质，并为此开发了一个专用模型，使用BERT达到了74.86%的准确率。对该数据集的分析证明，语言特征、个人资料特征和衍生模型可用于区分不同的人格特质。此外，我们的研究结果表明，人格会影响社交媒体上的情感。这项研究有助于持续努力深入理解社交媒体上的人类行为与人格特征之间的关系，以应用于现实世界的应用，例如政治话语分析和公众舆论追踪。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
