Emotion-dependent language featuring depression

Chaoqing Yang; Xinying Zhang; Yuxuan Chen; Yunge Li; Shu Yu; Bingmei Zhao; Tao Wang; Lizhu Luo; Shan Gao

doi:10.1016/j.jbtep.2023.101883

Emotion-dependent language featuring depression

J Behav Ther Exp Psychiatry. 2023 Dec:81:101883. doi: 10.1016/j.jbtep.2023.101883. Epub 2023 May 27.

Authors

Chaoqing Yang¹, Xinying Zhang¹, Yuxuan Chen¹, Yunge Li², Shu Yu², Bingmei Zhao², Tao Wang³, Lizhu Luo⁴, Shan Gao⁵

Affiliations

¹ School of Foreign Languages, University of Electronic Science and Technology of China, Chengdu, China.
² The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
³ School of Psychology, Qufu Normal University, Qufu, China.
⁴ The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Singapore Institute for Clinical Sciences, A*STAR Research Entities, Singapore. Electronic address: lisall599@outlook.com.
⁵ School of Foreign Languages, University of Electronic Science and Technology of China, Chengdu, China; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China. Electronic address: gaoshan@uestc.edu.cn.

PMID: 37290350
DOI: 10.1016/j.jbtep.2023.101883

Abstract

Background and objectives: 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.

Methods: 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.

Results: 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.

Limitations: 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.

Conclusions: 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.

Keywords: Autobiographical memory; Depression; Emotion; Speech; Word use.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Anger
Depression*
Emotions
Humans
Language*
Linguistics