首页> 外文会议>International Conference in Advances in Electrical and Computer Technologies >Secret Life of Conjunctions: Correlation of Conjunction Words on Predicting Personality Traits from Social Media Using User-Generated Contents
【24h】

Secret Life of Conjunctions: Correlation of Conjunction Words on Predicting Personality Traits from Social Media Using User-Generated Contents

机译:连词的秘密生活:使用用户生成的内容来预测社交媒体人格特征的联合词语的相关性

获取原文

摘要

Large amount of textual, visual, and audio data are generating in social networking sites by the users nowadays. Social media users are generating these data in high increasing rate than any other time. Status updates/tweets, likes, comments, and shares/re-tweets are the basic features provided by the online social networking (OSN) sites. This paper utilizes the status updates of users to analyze and extract relevant natural language features to map them into predicting personality traits of those users. It is evident that using more features in a supervised learning system can predict more accurately. However, the linguistic features such as function words, character-level, word-level, structure-level features could be considered as relevant features for this case. While predicting the big five personality traits: openness-to-experience, conscientiousness, extraversion, agreeableness and neuroticism, the highly correlated features are determined applying feature selection algorithms. For experimentation, the research question is "What are the highly correlated features which are commonly found for all five personality traits?" In this paper, we have presented the experimental findings while determining the highly correlated features with the class and found that the percentage of "conjunction words" is always a common feature for each of the personality traits. The underlying (secret) relationship of this feature is analyzed in this paper.
机译:现在,大量文本,视觉和音频数据由用户在社交网站上产生。社交媒体用户的速度高于任何其他时间以高的速度生成这些数据。状态更新/推文,喜欢,评论和共享/重新推文是在线社交网络(OSN)站点提供的基本功能。本文利用用户的状态更新来分析和提取相关的自然语言特征,以将它们映射到预测这些用户的个性性状。很明显,在监督学习系统中使用更多特征可以更准确地预测。但是,诸如功能词,字符级,字级,结构级别特征等语言特征可以被视为这种情况的相关特征。在预测五个人格特质的同时:开放的经验,令人满意,倾向,令人满意和神经质,高度相关的特征是确定的应用特征选择算法。对于实验,研究问题是“所有五个人格特征常见的高度相关性是什么?”在本文中,我们介绍了实验结果,同时确定了与班级的高度相关特征,发现“结合词语”的百分比始终是每个人格特征的共同特征。本文分析了该特征的潜在(秘密)关系。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号