摘要
针对目前抑郁症阅读疗法施治对象的选取样本过于单一,私密性差,不能及时发现潜在患者,及时治疗的现状,提出了在大数据背景下基于"用户画像"的抑郁症阅读疗法新模式。该模式首先根据"伯恩斯抑郁症清单(BDC)"的内容来构造抑郁情绪的种子词,然后基于机器学习的方法,综合提炼用户网上行为和抑郁情绪的主观表露,构建用户抑郁情感词典。根据抑郁情感词典分析用户微博文本,计算其抑郁情感指数,得到"用户画像",进而推送相应的阅读治疗资源。基于"用户画像"的阅读疗法模式大大突破了受众范围,便于准确把握诊断、治疗时机,使患者在无意识状态下接受阅读治疗,减轻了患者经济和精神的双重压力,具有较高的社会价值和重要的现实意义。
In this paper, a new mode of bibliotherapy for depression based on user profile is put forward in view of the current situation of depression bibliotherapy with weakness such as samples selected too sim- ple, poor in privacy, and unable to detect and treat potential patients in a timely manner. This mode first constructed the seed words of depressive mood according to "The Burns Depression Checklist (BDC)", and then based on the machine learning method, comprehensively refined the subjective revealing of user's on- line behavior and depressive mood to construct the user's depressed emotional dictionary. According to the depressed emotional dictionary to analyze user's blog, calculate the depression emotional index, get the us- er profile, and then push the corresponding bibliotherapy resources. The bibliotherapy mode based on the user profile exceeded the audience scope, accurately grasp the timing of diagnosis and treatment, so that patients could accept bibliotherapy in an unconscious state, so as to reduce the patient's double pressure of economic and spirit.
出处
《大学图书馆学报》
CSSCI
北大核心
2017年第6期105-110,共6页
Journal of Academic Libraries
基金
2016年度校级哲学社会科学研究规划项目"新媒体环境下齐鲁传统文化经典藏读模式研究"(编号:XSK201615)的研究成果之一