{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:56:12Z","timestamp":1792000572804,"version":"4.4.0"},"reference-count":52,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T00:00:00Z","timestamp":1791936000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Depression is a prevalent mental health condition that significantly affects individuals\u2019 well-being and daily functioning, making early depressive symptom detection crucial.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study explores the potential of advancing speech analytics with dimensional sentiment analysis, using valence and arousal scoring to enhance depressive symptom detection in family caregivers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We recruited 116 family caregivers and assessed their depression levels using the Beck Depression Inventory-II. Participants provided verbal responses to 12 open-ended questions related to family resilience. The extracted words from the responses were analyzed for sentiment using the Chinese Valence-Arousal Words dictionary to determine their valence and arousal ratings. For each participant, we computed various summary statistics (e.g., mean, variance, quantiles) and frequency-domain statistics (e.g., spectral entropy, dominant frequency components) of valence and arousal distributions. These features were then used as predictors in nine different machine learning models evaluated under repeated 5-fold nested cross-validation with cost-sensitive cutoff selection, enabling robust classification of depression levels while prioritizing the identification of true positive cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our analysis showed that participants with higher depressive symptoms used words with more extreme valence and arousal scores than those with lower depressive symptoms. The models demonstrated strong predictive utility of these emotional dimensions: the quadratic discriminant analysis (QDA) model achieved an out-of-sample sensitivity of 0.725, a specificity of 0.563, and an AUC of 0.7. These findings support that valence and arousal analysis provides meaningful discriminative power for identifying depressive symptoms. Importantly, the high sensitivity achieved by these models ensures that individuals with elevated symptoms are more reliably detected, thereby reducing the risk of missed cases. Overall, the results suggest strong potential for automated depressive symptom detection based on emotional signal analysis, with cost-sensitive optimization reducing false negatives while maintaining clinically relevant specificity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec005\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study offers proof-of-concept evidence that emotional sentiment analysis of speech using valence and arousal scoring can capture linguistic markers associated with depressive symptoms among family caregivers. With further refinement and validation, this approach lays an important foundation for the development of more robust machine learning models and ultimately contributes to non-intrusive mental health screening tools.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0359556","type":"journal-article","created":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:33:57Z","timestamp":1791999237000},"page":"e0359556","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Using valence and arousal scoring in speech to detect depressive symptoms: A study on family caregivers"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0781-8166","authenticated-orcid":true,"given":"Mike K. 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Cambria","year":"2017"},{"issue":"6","key":"pone.0359556.ref031","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1037\/h0077714","article-title":"A circumplex model of affect","volume":"39","author":"JA Russell","year":"1980","journal-title":"J Pers Soc Psychol"},{"issue":"4","key":"pone.0359556.ref032","article-title":"Chinese EmoBank: Building Valence-Arousal Resources for Dimensional Sentiment Analysis","volume":"21","author":"L-H Lee","year":"2022","journal-title":"ACM Trans Asian Low-Resour Lang Inf Process"},{"issue":"1","key":"pone.0359556.ref033","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/0005-7916(94)90063-9","article-title":"Measuring emotion: the Self-Assessment Manikin and the Semantic Differential","volume":"25","author":"MM Bradley","year":"1994","journal-title":"J Behav Ther Exp Psychiatry"},{"key":"pone.0359556.ref034","doi-asserted-by":"crossref","unstructured":"Mohammad S. Obtaining Reliable Human Ratings of Valence, Arousal, and Dominance for 20,000 English Words. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018. p. 174\u201384.","DOI":"10.18653\/v1\/P18-1017"},{"issue":"6","key":"pone.0359556.ref035","doi-asserted-by":"crossref","DOI":"10.1192\/bjo.2020.99","article-title":"Risk of depression in family caregivers: unintended consequence of COVID-19","volume":"6","author":"S Gallagher","year":"2020","journal-title":"BJPsych Open"},{"issue":"1","key":"pone.0359556.ref036","first-page":"130","article-title":"Measuring family resilience of Chinese family caregivers: Psychometric evaluation of the Family Resilience Assessment Scale","volume":"71","author":"AMY Chu","year":"2021","journal-title":"Fam Relat"},{"issue":"2","key":"pone.0359556.ref037","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1080\/17538157.2021.1988955","article-title":"Predicting the burden of family caregivers from their individual characteristics","volume":"47","author":"MKP So","year":"2022","journal-title":"Inform Health Soc Care"},{"key":"pone.0359556.ref038","article-title":"Beck Depression Inventory-II","author":"AT Beck","year":"1996","journal-title":"Psychol Assess"},{"issue":"6","key":"pone.0359556.ref039","first-page":"762","article-title":"Application of Chinese version of Beck Depression Inventory-II to Chinese first-year college students","volume":"20","author":"W Yang","year":"2012","journal-title":"Chin J Clin Psychol"},{"issue":"4","key":"pone.0359556.ref040","doi-asserted-by":"crossref","DOI":"10.2196\/13202","article-title":"Transdiagnostic Mobile Health: Smartphone Intervention Reduces Depressive Symptoms in People With Mood and Psychotic Disorders","volume":"6","author":"D Ben-Zeev","year":"2019","journal-title":"JMIR Ment Health"},{"issue":"1","key":"pone.0359556.ref041","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s12144-024-07110-1","article-title":"Which types of family caregivers are more prone to developing depression? 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In: Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access. 2021. p. 192\u2013203.","DOI":"10.18653\/v1\/2021.clpsych-1.21"},{"issue":"2","key":"pone.0359556.ref043","first-page":"179","article-title":"Relationship between family resilience, severity of depression and self-esteem in adults with major depressive disorder","volume":"14","author":"C Chettiar","year":"2023","journal-title":"Indian J Posit Psychol"},{"issue":"2","key":"pone.0359556.ref044","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1007\/s42001-024-00270-x","article-title":"Applications of Bayesian networks in assessing the effects of family resilience on caregiver behavioral problems, depressive symptoms, and burdens","volume":"7","author":"AMY Chu","year":"2024","journal-title":"J Comput Soc Sc"},{"issue":"1","key":"pone.0359556.ref045","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1545-5300.2003.00001.x","article-title":"Family resilience: a framework for clinical practice","volume":"42","author":"F Walsh","year":"2003","journal-title":"Fam Process"},{"key":"pone.0359556.ref046","doi-asserted-by":"crossref","unstructured":"Lee JL, Chen L, Lam C, Lau CM, Tsui T-H. PyCantonese: Cantonese Linguistics and NLP in Python. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference. 2022. p. 6607\u201311.","DOI":"10.63317\/5msekdgipzig"},{"issue":"2","key":"pone.0359556.ref047","first-page":"199","article-title":"Negation as a means for emotion regulation?","volume":"11","author":"C Herbert","year":"2011","journal-title":"Emotion"},{"issue":"2","key":"pone.0359556.ref048","doi-asserted-by":"crossref","first-page":"572","DOI":"10.3390\/app16020572","article-title":"Spectrogram Features for Audio and Speech Analysis","volume":"16","author":"I McLoughlin","year":"2026","journal-title":"Appl Sci"},{"key":"pone.0359556.ref049","doi-asserted-by":"crossref","unstructured":"Yu L-C, Lee L-H, Hao S, Wang J, He Y, Hu J, et al. Building Chinese Affective Resources in Valence-Arousal Dimensions. 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