Insight into computational techniques in detecting depression in text
DOI:
https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1363Keywords:
Computing, Depression, RevisionAbstract
Objective: Review the literature on depression detection in texts, focusing on machine learning and natural language processing techniques.
Method: Analysis of studies using advanced computational techniques and dictionaries of depression-indicative words, considering the integration of machine learning methods, natural language processing, and mental health resources.
Results: Most works use advanced computational techniques and specific dictionaries, but there is little integration of linguistics and mental health in the models. A gap in incorporating the cultural and regional context of written language was observed.
Conclusion: Emphasizes the need to include linguistics to consider the cultural and regional context and increase the use of mental health resources in identifying depression in texts, improving the precision and effectiveness of detection tools.
References
de Souza RC. O que é psicologia. [Internet]. 2024 [citado 10 jan 2024]. Disponível em: https://www.oasisbr.ibict.br/vufind/Record/UFAM-1_4b00bd2b922bad486fde3ef8829cd87b
Rice F, Riglin L, Lomax T, Souter E, Potter R, Smith D, Thapar A, Thapar A. Diferenças entre adolescentes e adultos nos perfis de sintomas de depressão maior. [Internet]. 2019 [citado 08 nov 2023]. doi: https://doi.org/10.1016/j.jad.2018.09.015. DOI: https://doi.org/10.1016/j.jad.2018.09.015
Tolentino J, Schmidt S. Critérios do DSM-5 e gravidade da depressão: implicações para a prática clínica. Fronteiras em Psiquiatria. 2018;9 [citado 08 nov 2023]. doi: https://doi.org/10.3389/fpsyt.2018.00450. DOI: https://doi.org/10.3389/fpsyt.2018.00450
Vermeulen A, Vandebosch H, Heirman W. #Sorrindo, #desabafando ou ambos? Compartilhamento social de emoções por adolescentes nas redes sociais. Computação. Zumbir. Comporte-se. 2018;84:211-219. doi: https://doi.org/10.1016/j.chb.2018.02.022. DOI: https://doi.org/10.1016/j.chb.2018.02.022
Trivedi M. Transtorno Depressivo Maior na Atenção Primária: Estratégias para Identificação. The Journal of Clinical Psychiatry. 2020;81(2) [citado 08 nov 2023]. doi: https://doi.org/10.4088/jcp.ut17042br1c.
Amanat A, Rizwan M, Javed A, Abdelhaq M, Alsaqour R, Pandya S, Uddin M. Aprendizado profundo para detecção de depressão a partir de dados textuais. Eletrônicos. 2022 [citado 08 nov 2023]. doi: https://doi.org/10.3390/electronics11050676. DOI: https://doi.org/10.3390/electronics11050676
World Health Organization. Depression and Other Common Mental Disorders: Global Health Estimates. World Health Organization; 2017.
Silva FA, Souza RS. Natural Language Processing for Social Media Text Analysis in Detecting Depression. Journal of Medical Internet Research. 2020;22(6)
Oliveira LM, Cunha AB. Cultural Adaptation and Validation of Depression Screening Tools in Brazil. International Journal of Mental Health Systems. 2019;13:45.
Manning CD, Raghavan P, Schütze H. Introduction to Information Retrieval. Cambridge University Press; 2008. DOI: https://doi.org/10.1017/CBO9780511809071
Jurafsky D, Martin JH. Speech and Language Processing. 3rd ed. Pearson; 2019.
Kitchenham B, Brereton P. A systematic review of systematic review process research in software engineering. Information and Software Technology. 2013;55(12):2049-2075. DOI: https://doi.org/10.1016/j.infsof.2013.07.010
Saravanan T, Jhaideep T, Bindu NH. Detecting depression using Hybrid models created using Google's BERT and Facebook's Fast Text Algorithms. Proceedings of the 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). 2022 Apr 28; Greater Noida, India. p. 415-421. doi: 10.1109/ICACITE53722.2022.9823581. DOI: https://doi.org/10.1109/ICACITE53722.2022.9823581
de Carvalho VF, Giacon B, Nascimento C, Nogueira BM. Aprendizado de Máquina para Identificação de Ideação Suicida no Twitter para a Língua Portuguesa. In: Cerri R, Prati RC, editores. Sistemas Inteligentes. BRACIS 2020. Notas de aula em Ciência da Computação. vol. 12319. Springer, Cham; 2020. p. 123-131. doi: 10.1007/978-3-030-61377-8_37. DOI: https://doi.org/10.1007/978-3-030-61377-8_37
Malhotra A, Jindal R. Deep learning techniques for suicide and depression detection from online social media: A scoping review. Applied Soft Computing. 2022;130:109713. doi: 10.1016/j.asoc.2022.109713. DOI: https://doi.org/10.1016/j.asoc.2022.109713
Vieira S, Liang X, Guiomar R, Mechelli A. Can we predict who will benefit from cognitive-behavioural therapy? A systematic review and meta-analysis of machine learning studies. Clinical Psychology Review. 2022;97:102193. doi: 10.1016/j.cpr.2022.102193. DOI: https://doi.org/10.1016/j.cpr.2022.102193
Li G, Li B, Huang L, Hou S. Automatic Construction of a Depression-Domain Lexicon Based on Microblogs: Text Mining Study. JMIR Med Inform. 2020 Jun 23;8(6)
doi: 10.2196/17650. DOI: https://doi.org/10.2196/17650
Lima GMdA. Detecção de indícios de depressão em textos curtos usando transferência de conhecimento. [Internet]. 2023 [citado 15 jan 2024]. Disponível em: https://www.oasisbr.ibict.br/vufind/Record/UFAM-1_4b00bd2b922bad486fde3ef8829cd87b
Cha J, Kim S, Park E. A lexicon-based approach to examine depression detection in social media: the case of Twitter and university community. Humanit Soc Sci Commun. 2022;9(1):325. doi: 10.1057/s41599-022-01313-2. DOI: https://doi.org/10.1057/s41599-022-01313-2
O que é API. [Internet]. 2024 [citado 01 maio 2024]. Disponível em: https://encurtador.com.br/nUCzg
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