Cariola Laura A, Hinduja Saurabh, Bilalpur Maneesh, Sheeber Lisa B, Allen Nicholas, Morency Louis-Philippe, Cohn Jeffrey F
Clinical and Health Psychology, University of Edinburgh, Edinburgh, UK.
Department of Psychology, University of Pittsburgh, Pittsburgh, USA.
Int Conf Affect Comput Intell Interact Workshops. 2022 Oct;2022. doi: 10.1109/acii55700.2022.9953886. Epub 2022 Nov 25.
This preliminary study applied a computer-assisted quantitative linguistic analysis to examine the effectiveness of language-based classification models to discriminate between mothers (n = 140) with and without history of treatment for depression (51% and 49%, respectively). Mothers were recorded during a problem-solving interaction with their adolescent child. Transcripts were manually annotated and analyzed using a dictionary-based, natural-language program approach (Linguistic Inquiry and Word Count). To assess the importance of linguistic features to correctly classify history of depression, we used Support Vector Machines (SVM) with interpretable features. Using linguistic features identified in the empirical literature, an initial SVM achieved nearly 63% accuracy. A second SVM using only the top 5 highest ranked SHAP features improved accuracy to 67.15%. The findings extend the existing literature base on understanding language behavior of depressed mood states, with a focus on the linguistic style of mothers with and without a history of treatment for depression and its potential impact on child development and trans-generational transmission of depression.
这项初步研究应用了计算机辅助定量语言分析，以检验基于语言的分类模型区分有抑郁症治疗史和无抑郁症治疗史的母亲（分别为140名，比例分别为51%和49%）的有效性。在母亲与青春期孩子进行解决问题互动时对她们进行了录音。使用基于词典的自然语言程序方法（语言查询与字数统计）对手稿进行了人工注释和分析。为了评估语言特征对正确分类抑郁症病史的重要性，我们使用了具有可解释特征的支持向量机（SVM）。利用实证文献中确定的语言特征，初始支持向量机的准确率接近63%。仅使用排名前5的最高SHAP特征的第二个支持向量机将准确率提高到了67.15%。这些发现扩展了现有关于理解抑郁情绪状态语言行为的文献基础，重点关注有和没有抑郁症治疗史的母亲的语言风格及其对儿童发育和抑郁症代际传播的潜在影响。
