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Sensing Users’ Emotional Intelligence in Social Networks

机译:传感用户在社交网络中的情商

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摘要

Social networks have integrated into the daily lives of most people in the way of interactions and of lifestyles. The users' identity, relationships, or other characteristics can be explored from the social networking data, in order to provide personalized services to the users. In this article, we focus on predicting the user's emotional intelligence (EI) based on social networking data. As an essential facet of users' psychological characteristics, EI plays an important role on well-being, interpersonal relationships, and overall success in people's life. Perception of EI contributes to predicting one's behavior or group behavior. Most existing work on predicting people's EI is based on questionnaires that may collect dishonest answers or unconscientious responses, thus leading in potentially inaccurate prediction results. In this article, we are motivated to propose EI prediction models based on the sentiment analysis of social networking data. The models are represented by four dimensions, including self-awareness, self-regulation, self-motivation, and social relationships. The EI of a user is then measured by four numerical values or the sum of them. In the experiments, we predict the EIs of over a hundred thousand users based on one of the largest social networks of China, Weibo. The predicting results demonstrate the effectiveness of our models. The results show that the distribution of the four EI's dimensions of users is roughly normal. The results also indicate that EI scores of females are generally higher than males' EI scores. This is consistent with previous findings. In addition, the four dimensions of EI are correlated. We finally analyze the advantages and the disadvantages of our models in predicting users' EI with social networking data.
机译:社交网络纳入了大多数人的日常生活,以互动和生活方式。可以从社交网络数据探索用户的身份,关系或其他特征,以便向用户提供个性化服务。在本文中,我们专注于根据社交网络数据预测用户的情绪智能(EI)。作为用户心理特征的重要方面,EI对幸福,人际关系和人民生活中的总体成功起着重要作用。对EI的看法有助于预测一个人的行为或团体行为。大多数现有的预测人民EI的工作基于可能收集不诚实的答案或非苛刻反应的问卷,从而导致可能不准确的预测结果。在本文中,我们有动力基于社交网络数据的情感分析提出EI预测模型。该模型由四个维度表示,包括自我意识,自我监管,自我激励和社会关系。然后通过四个数值或它们的总和测量用户的EI。在实验中,我们根据中国最大的社交网络之一,预测超过十万用户的EIS。预测结果表明了我们模型的有效性。结果表明,四个EI的用户维度的分布大致正常。结果还表明,女性的EI分数通常高于男性的EI分数。这与以前的发现一致。此外,EI的四个维度是相关的。我们终于分析了我们在通过社交网络数据预测用户EI的模型的优势和缺点。

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  • 作者单位

    Beijing Jiaotong Univ Beijing Key Lab Secur & Privacy Intelligent Trans Beijing 100044 Peoples R China;

    Tianjin Univ Coll Intelligence & Comp Tianjin Key Lab Adv Networking Tianjin 300350 Peoples R China;

    Norwegian Univ Sci & Technol Dept Comp Sci N-7491 Trondheim Norway;

    Beijing Jiaotong Univ Beijing Key Lab Secur & Privacy Intelligent Trans Beijing 100044 Peoples R China;

    Beijing Jiaotong Univ Beijing Key Lab Secur & Privacy Intelligent Trans Beijing 100044 Peoples R China;

    Beijing Jiaotong Univ Beijing Key Lab Secur & Privacy Intelligent Trans Beijing 100044 Peoples R China|KAUST Div Comp Elect & Math Sci & Engn CEMSE Thuwal 239556900 Saudi Arabia;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Emotional intelligence (EI); sentiment analysis; social networks; user profile;

    机译:情商(EI);情感分析;社交网络;用户个人资料;

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