Yang C, Zhang X, Chen Y, Li Y, Yu S, Zhao B, Wang T, Luo L, Gao S. Emotion-dependent language featuring depression.
J Behav Ther Exp Psychiatry 2023;
81:101883. [PMID:
37290350 DOI:
10.1016/j.jbtep.2023.101883]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 04/06/2023] [Accepted: 05/27/2023] [Indexed: 06/10/2023]
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.
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