Mood disorder patients\' language features on their microblogs - 成果

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Mood disorder patients' language features on their microblogs

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作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Tai Wang;Zongkui Zhou;Tingshao Zhu;Yang Wei

通讯作者：

Wang, Tai

作者机构：

[Tai Wang] National Engineering Research Center for E.learning, Central China Normal University, Science Hall, Room 417, Luoyu Road 152, Wuhan, Hubei, China

[Yang Wei] National Engineering Research Center for E.learning, Central China Normal University, Science Hall, Room 302, Luoyu Road 152, Wuhan, Hubei, China

[Tingshao Zhu] Computational CyberPsychology Lab, Institute of Psychology, Chinese Academy of Sciences, Lincui Road 16, Chaoyang District, Beijing, China

[Zongkui Zhou] Department of Psychology, Key Laboratory of Ministry of Education of Juveniles CyberPsychology and Behaviour, Central China Normal University, Room 703, Luoyu Road 152, Wuhan, Hubei, China

通讯机构：

National Engineering Research Center for E.learning, Central China Normal University, Science Hall, Room 417, Luoyu Road 152, Wuhan, Hubei, China

语种：

英文

关键词：

mood disorders;microblogs;language features;embedded systems;mood disorder patients;social networking sites;SNS;positive emotions;emotion expressions;negative emotions;anger;mood disorder diagnosis.

期刊：

International Journal of Embedded Systems

ISSN：

1741-1068

年：

2015

卷：

期：

页码：

34-42

DOI：

10.1504/IJES.2015.066140

机构署名：

本校为第一且通讯机构

院系归属：

心理学院

国家数字化学习工程技术研究中心

摘要：

People's language features are exhibited on their online social network websites, such as Twitter, Weibo in Sina or ShuoShuo in QQ (a former version of microblog). Several leading labs have already made remarkable breakthroughs in the area of collecting and analysing texts generated by a huge population. In this paper, a novel research topic is presented, with the assumption that different kinds of people may exhibit their unique language features, especially mood disorder patients and normal people. The best efforts have been carried out to verify this assumption. Three mood disorder patients...

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