Automated depression detection in text data: leveraging lexical features, phonesthemes embedding, and roberta transformer model
Indexed keywords SciVal Topics Metrics Funding details Abstract Depression is a prevalent mental disorder characterized by persistent sadness, lack of interest, and diminished pleasure. Detecting depression is crucial for timely intervention and support. In this paper, we address the task of depress...
- Autores:
-
Martinez, Elizabeth
Cuadrado, Juan
Martinez-Santos, Juan Carlos
Peña, Daniel
Puertas, Edwin
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12583
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12583
https://ceur-ws.org/Vol-3496/mentalriskes-paper15.pdf
- Palabra clave:
- Depression
Lexical Features
Mental Risk
Phonesthemes Embedding
Transformers
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Automated depression detection in text data: leveraging lexical features, phonesthemes embedding, and roberta transformer model |
| title |
Automated depression detection in text data: leveraging lexical features, phonesthemes embedding, and roberta transformer model |
| spellingShingle |
Automated depression detection in text data: leveraging lexical features, phonesthemes embedding, and roberta transformer model Depression Lexical Features Mental Risk Phonesthemes Embedding Transformers LEMB |
| title_short |
Automated depression detection in text data: leveraging lexical features, phonesthemes embedding, and roberta transformer model |
| title_full |
Automated depression detection in text data: leveraging lexical features, phonesthemes embedding, and roberta transformer model |
| title_fullStr |
Automated depression detection in text data: leveraging lexical features, phonesthemes embedding, and roberta transformer model |
| title_full_unstemmed |
Automated depression detection in text data: leveraging lexical features, phonesthemes embedding, and roberta transformer model |
| title_sort |
Automated depression detection in text data: leveraging lexical features, phonesthemes embedding, and roberta transformer model |
| dc.creator.fl_str_mv |
Martinez, Elizabeth Cuadrado, Juan Martinez-Santos, Juan Carlos Peña, Daniel Puertas, Edwin |
| dc.contributor.author.none.fl_str_mv |
Martinez, Elizabeth Cuadrado, Juan Martinez-Santos, Juan Carlos Peña, Daniel Puertas, Edwin |
| dc.subject.keywords.spa.fl_str_mv |
Depression Lexical Features Mental Risk Phonesthemes Embedding Transformers |
| topic |
Depression Lexical Features Mental Risk Phonesthemes Embedding Transformers LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
Indexed keywords SciVal Topics Metrics Funding details Abstract Depression is a prevalent mental disorder characterized by persistent sadness, lack of interest, and diminished pleasure. Detecting depression is crucial for timely intervention and support. In this paper, we address the task of depression detection in text data, focusing on binary classification and regression. We present our approach, leveraging a dataset comprising labeled messages from Telegram groups related to mental disorders. We begin by exploring the existing literature on depression detection, highlighting the challenges faced and the methods employed. Our approach involves data pre-processing, lexical feature extraction, phonesthemes embedding, and using the RoBERTa transformer model. We achieved promising results in the training phase through rigorous experimentation and model refinement. However, we encountered challenges upon evaluating our approach in the MentalRiskEs evaluation. We identified areas for improvement, particularly in latency and speed of detection for real-time monitoring of depression-related risks. This research contributes to the ongoing efforts in automating depression detection and provides insights into the potential of text analysis techniques for mental health assessment. We remain committed to further enhancing our methodology and advancing the field to improve the well-being of individuals affected by depression. |
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2023 |
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2023-12-06T16:09:43Z |
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2023-12-06T16:09:43Z |
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2023-12-05 |
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2023-12-05 |
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Martinez, E., Cuadrado, J., Peña, D., Martinez-Santos, J. C., & Puertas, E. (2023). Automated Depression Detection in Text Data: Leveraging Lexical Features, phonesthemes Embedding, and RoBERTa Transformer Model. In IberLEF (Working Notes). CEUR Workshop Proceedings. |
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https://hdl.handle.net/20.500.12585/12583 |
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https://ceur-ws.org/Vol-3496/mentalriskes-paper15.pdf |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Martinez, E., Cuadrado, J., Peña, D., Martinez-Santos, J. C., & Puertas, E. (2023). Automated Depression Detection in Text Data: Leveraging Lexical Features, phonesthemes Embedding, and RoBERTa Transformer Model. In IberLEF (Working Notes). CEUR Workshop Proceedings. Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/12583 https://ceur-ws.org/Vol-3496/mentalriskes-paper15.pdf |
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eng |
| language |
eng |
| dc.relation.iscitedby.none.fl_str_mv |
Martinez, E., Cuadrado, J., Peña, D., Martinez-Santos, J. C., & Puertas, E. (2023). Automated Depression Detection in Text Data: Leveraging Lexical Features, phonesthemes Embedding, and RoBERTa Transformer Model. In IberLEF (Working Notes). CEUR Workshop Proceedings. |
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14 páginas |
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Campus Tecnológico |
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Maestría en Ingeniería |
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Martinez, Elizabeth4ebda059-55c6-4e72-8ce5-81181da731b4Cuadrado, Juan73b693c6-9993-4025-9268-0f1bbe13b105Martinez-Santos, Juan Carlos5c958644-c78d-401d-8ba9-bbd39fe77318Peña, Danield014a16e-e795-4ca4-b9d0-0fb7d9fb850cPuertas, Edwin9e3c6f17-9041-40e3-a5fb-929a21d229012023-12-06T16:09:43Z2023-12-06T16:09:43Z2023-12-052023-12-05Martinez, E., Cuadrado, J., Peña, D., Martinez-Santos, J. C., & Puertas, E. (2023). Automated Depression Detection in Text Data: Leveraging Lexical Features, phonesthemes Embedding, and RoBERTa Transformer Model. In IberLEF (Working Notes). CEUR Workshop Proceedings.https://hdl.handle.net/20.500.12585/12583https://ceur-ws.org/Vol-3496/mentalriskes-paper15.pdfUniversidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarIndexed keywords SciVal Topics Metrics Funding details Abstract Depression is a prevalent mental disorder characterized by persistent sadness, lack of interest, and diminished pleasure. Detecting depression is crucial for timely intervention and support. In this paper, we address the task of depression detection in text data, focusing on binary classification and regression. We present our approach, leveraging a dataset comprising labeled messages from Telegram groups related to mental disorders. We begin by exploring the existing literature on depression detection, highlighting the challenges faced and the methods employed. Our approach involves data pre-processing, lexical feature extraction, phonesthemes embedding, and using the RoBERTa transformer model. We achieved promising results in the training phase through rigorous experimentation and model refinement. However, we encountered challenges upon evaluating our approach in the MentalRiskEs evaluation. We identified areas for improvement, particularly in latency and speed of detection for real-time monitoring of depression-related risks. This research contributes to the ongoing efforts in automating depression detection and provides insights into the potential of text analysis techniques for mental health assessment. We remain committed to further enhancing our methodology and advancing the field to improve the well-being of individuals affected by depression.Universidad Tecnológica de Bolívar14 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Iberian Languages Evaluation ForumAutomated depression detection in text data: leveraging lexical features, phonesthemes embedding, and roberta transformer modelinfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1DepressionLexical FeaturesMental RiskPhonesthemes EmbeddingTransformersLEMBMartinez, E., Cuadrado, J., Peña, D., Martinez-Santos, J. C., & Puertas, E. (2023). Automated Depression Detection in Text Data: Leveraging Lexical Features, phonesthemes Embedding, and RoBERTa Transformer Model. In IberLEF (Working Notes). CEUR Workshop Proceedings.Cartagena de IndiasCampus TecnológicoMaestría en IngenieríaPúblico generalWasserman, D., Iosue, M., Wuestefeld, A., & Carli, V. (2020). Adaptation of evidence‐based suicide prevention strategies during and after the COVID‐19 pandemic. World psychiatry, 19(3), 294-306.Kim, H., Lee, S., Lee, S., Hong, S., Kang, H., & Kim, N. (2019). Depression prediction by using ecological momentary assessment, actiwatch data, and machine learning: observational study on older adults living alone. JMIR mHealth and uHealth, 7(10), e14149.Bolton, J. M., Gunnell, D., & Turecki, G. (2015). Suicide risk assessment and intervention in people with mental illness. Bmj, 351.Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349(6245), 261-266.Puertas, E., Moreno-Sandoval, L. G., Plaza-del Arco, F. M., Alvarado-Valencia, J. A., Pomares-Quimbaya, A., & Alfonso, L. (2019). Bots and gender profiling on twitter using sociolinguistic features. CLEF (Working Notes), 1-8.Moreno-Sandoval, L. G., Puertas, E., Plaza-del-Arco, F. M., Pomares-Quimbaya, A., Alvarado-Valencia, J. A., & Alfonso, L. (2019). Celebrity profiling on twitter using sociolinguistic. CLEF (Working Notes).Puertas, E., Moreno-Sandoval, L. G., Redondo, J., Alvarado-Valencia, J. A., & Pomares-Quimbaya, A. (2021). Detection of sociolinguistic features in digital social networks for the detection of communities. Cognitive Computation, 13, 518-537.Kabir, M., Ahmed, T., Hasan, M. B., Laskar, M. T. R., Joarder, T. K., Mahmud, H., & Hasan, K. (2023). DEPTWEET: A typology for social media texts to detect depression severities. Computers in Human Behavior, 139, 107503.Shoaib, M., Shah, B., Ei-Sappagh, S., Ali, A., Ullah, A., Alenezi, F., ... & Ali, F. (2023). An advanced deep learning models-based plant disease detection: A review of recent research. Frontiers in Plant Science, 14, 1158933.Mármol-Romero, A. M., Moreno-Muñoz, A., Plaza-del-Arco, F. M., Molina-González, M. D., Martín-Valdivia, M. T., Ureña-López, L. A., & Montejo-Raéz, A. (2023). Overview of MentalriskES at IberLEF 2023: Early Detection of Mental Disorders Risk in Spanish. Procesamiento del Lenguaje Natural, 71, 329-350.Puertas, E. A. (2023). Análisis de elementos fonéticos y elementos emocionales para predecir la polaridad en fuentes de microblogging. Recuperado de: http://hdl.handle.net/10554/63548.Pérez, J. M., Furman, D. A., Alemany, L. A., & Luque, F. (2021). RoBERTuito: A pre-trained language model for social media text in Spanish. ArXiv. /abs/2111.09453Huang, S., Cai, N., Pacheco, P. P., Narrandes, S., Wang, Y., & Xu, W. (2018). Applications of support vector machine (SVM) learning in cancer genomics. Cancer genomics & proteomics, 15(1), 41-51.Puertas, E., & Martinez-Santos, J. C. (2021). Phonetic detection for hate speech spreaders on Twitter.De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. In Proceedings of the international AAAI conference on web and social media (Vol. 7, No. 1, pp. 128-137).Di Cara, N. H., Maggio, V., Davis, O. S., & Haworth, C. M. (2023). Methodologies for monitoring mental health on Twitter: systematic review. Journal of Medical Internet Research, 25, e42734.Burdisso, S. G., Errecalde, M., & Montes-y-Gómez, M. (2019). A text classification framework for simple and effective early depression detection over social media streams. Expert Systems with Applications, 133, 182-197.Chiong, R., Budhi, G. S., Dhakal, S., & Chiong, F. (2021). A textual-based featuring approach for depression detection using machine learning classifiers and social media texts. Computers in Biology and Medicine, 135, 104499.Amanat, A., Rizwan, M., Javed, A. R., Abdelhaq, M., Alsaqour, R., Pandya, S., & Uddin, M. (2022). Deep learning for depression detection from textual data. Electronics, 11(5), 676.Babu, N. V., & Kanaga, E. G. M. (2022). Sentiment analysis in social media data for depression detection using artificial intelligence: a review. SN Computer Science, 3, 1-20.Mustafa, R. U., Ashraf, N., Ahmed, F. S., Ferzund, J., Shahzad, B., & Gelbukh, A. (2020). A multiclass depression detection in social media based on sentiment analysis. In 17th International Conference on Information Technology–New Generations (ITNG 2020) (pp. 659-662). 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