• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEICE transactions on information and systems >Machine Learning-Based Approach for Depression Detection in Twitter Using Content and Activity Features
【24h】

Machine Learning-Based Approach for Depression Detection in Twitter Using Content and Activity Features

机译：基于机器学习的抑郁症方法，用于使用内容和活动功能的推特中的抑郁检测方法

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Social media channels, such as Facebook, Twitter, and Instagram, have altered our world forever. People are now increasingly connected than ever and reveal a sort of digital persona. Although social media certainly has several remarkable features, the demerits are undeniable as well. Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets. For this purpose, we trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from his/her activities in the network and tweets. The results showed that the more features are used, the higher are the accuracy and F-measure scores in detecting depressed users. This method is a data-driven, predictive approach for early detection of depression or other mental illnesses. This study's main contribution is the exploration part of the features and its impact on detecting the depression level.
机译：社交媒体渠道，例如Facebook，Twitter和Instagram，永远改变了我们的世界。人们现在越来越联系，而且揭示了一种数字角色。虽然社交媒体当然有几种显着的特征，但缺点也是不可否认的。最近的研究表明，社交媒体网站的高效与抑郁症之间的高度之间存在相关性。本研究旨在利用机器学习技术，用于基于两者，他/她的网络行为和推文来检测可能的抑制推特用户。为此目的，我们培训并测试了分类器，以区分用户是否抑制或不使用网络和推文中提取的功能提取的功能。结果表明，使用更多的特征，检测抑制用户的准确性和F测量分数越高。该方法是一种数据驱动的预测方法，用于早期检测抑郁或其他精神疾病。本研究的主要贡献是特征的勘探部分及其对检测抑郁水平的影响。

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