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Emotion-dependent language featuring depression

心理学 萧条(经济学) 心理治疗师 认知心理学 临床心理学 精神分析 宏观经济学 经济
作者
Chaoqing Yang,Xinying Zhang,Yuxuan Chen,Yunge Li,Shu Yu,Bingmei Zhao,Tao Wang,Lizhu Luo,Shan Gao
出处
期刊:Journal of Behavior Therapy and Experimental Psychiatry [Elsevier BV]
卷期号:81: 101883-101883 被引量:9
标识
DOI:10.1016/j.jbtep.2023.101883
摘要

Understanding language features of depression contributes to the detection of the disorder. Considering that depression is characterized by dysfunctions in emotion and individuals with depression often show emotion-dependent cognition, the present study investigated the speech features and word use of emotion-dependent narrations in patients with depression.Forty depression patients and forty controls were required to narrate self-relevant memories under five basic human emotions (i.e., sad, angry, fearful, neutral, and happy). Recorded speech and transcribed texts were analyzed.Patients with depression, as compared to non-depressed individuals, talked slower and less. They also performed differently in using negative emotion, work, family, sex, biology, health, and assent words regardless of emotion manipulation. Moreover, the use of words such as first person singular pronoun, past tense, causation, achievement, family, death, psychology, impersonal pronoun, quantifier and preposition words displayed emotion-dependent differences between groups. With the involvement of emotion, linguistic indicators associated with depressive symptoms were identified and explained 71.6% variances of depression severity.Word use was analyzed based on the dictionary which does not cover all the words spoken in the memory task, resulting in text data loss. Besides, a relatively small number of depression patients were included in the present study and therefore the results need confirmation in future research using big emotion-dependent data of speech and texts.Our findings suggest that consideration of different emotional contexts is an effective means to improve the accuracy of depression detection via the analysis of word use and speech features.
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