DP-Symptom-Identifier: uma estratégia para classificar sintomas de depressão utilizando um conjunto de dados textuais na língua portuguesa
Resumo
A depressão é um distúrbio psicológico que afeta milhões de pessoas no mundo, indiferente à idade, classe social ou nacionalidade. Diferentes técnicas tem sido exploradas para analisar e reconhecer sintomas depressivos na literatura, como técnicas de Processamento de Linguagem Natural e Análise de Sentimentos. Entretanto, para o português brasileiro, poucos estudos tem proposto datasets para a classificação de sintomas da depressão. Neste artigo, propomos uma estratégia chamada DP-Symptom-Identifier para coletar tweets e criar um novo dataset com sentenças que possuem sintomas da depressão. Experimentos iniciais usando diferentes algoritmos obtiveram um alto desempenho preditivo, o que mostra que as pesquisas nesta área são promissoras.Referências
Aldarwish, M. M. and Ahmad, H. F. (2017). Predicting depression levels using social media posts. In 2017 IEEE 13th International Symposium on Autonomous Decentralized System (ISADS), pages 277–280.
APA (2013). Diagnostic and statistical manual of mental disorders : DSM-5. American Psychiatric Association Arlington, VA, 5th ed. edition.
Birjali, M., Beni-Hssane, A., and Erritali, M. (2017). A method proposed for estimating depressed feeling tendencies of social media users utilizing their data. pages 413–420. Springer International Publishing.
Casani, V., Mantovani, R. G., Souza, A. C. C., and Souza, F. C. M. (2021). Identificação de perfis depressivos em redes sociais utilizando aprendizado de máquina: um mapeamento sistemático. Computer on The Beach, (12).
Flach, P., Hernandez-Orallo, J., and Ferri, C. (2011). A coherent interpretation of auc as a measure of aggregated classification performance. pages 657–664.
Hassan, A. U., Hussain, J., Hussain, M., Sadiq, M., and Lee, S. (2017). Sentiment analysis of social networking sites (sns) data using machine learning approach for the measurement of depression. In 2017 International Conference on Information and Communication Technology Convergence (ICTC), pages 138–140.
Islam, M. R., Kamal, A. R. M., Sultana, N., Islam, R., Moni, M. A., and ulhaq, A. (2018). Detecting depression using k-nearest neighbors (knn) classification technique. In 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), pages 1–4.
Keumhee Kang, Chanhee Yoon, and Eun Yi Kim (2016). Identifying depressive users in twitter using multimodal analysis. In 2016 International Conference on Big Data and Smart Computing (BigComp), pages 231–238.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1):1–167.
Ma, L., Wang, Z., and Zhang, Y. (2017). Extracting depression symptoms from social networks and web blogs via text mining. pages 325–330. Springer International Publishing.
Organization, W. H. et al. (2017). Depression and other common mental disorders: global health estimates. Technical report, World Health Organization.
Rosa, R. L., Schwartz, G. M., Ruggiero, W. V., and Rodríguez, D. Z. (2019). A knowledge-based recommendation system that includes sentiment analysis and deep learning. IEEE Transactions on Industrial Informatics, 15(4):2124–2135.
APA (2013). Diagnostic and statistical manual of mental disorders : DSM-5. American Psychiatric Association Arlington, VA, 5th ed. edition.
Birjali, M., Beni-Hssane, A., and Erritali, M. (2017). A method proposed for estimating depressed feeling tendencies of social media users utilizing their data. pages 413–420. Springer International Publishing.
Casani, V., Mantovani, R. G., Souza, A. C. C., and Souza, F. C. M. (2021). Identificação de perfis depressivos em redes sociais utilizando aprendizado de máquina: um mapeamento sistemático. Computer on The Beach, (12).
Flach, P., Hernandez-Orallo, J., and Ferri, C. (2011). A coherent interpretation of auc as a measure of aggregated classification performance. pages 657–664.
Hassan, A. U., Hussain, J., Hussain, M., Sadiq, M., and Lee, S. (2017). Sentiment analysis of social networking sites (sns) data using machine learning approach for the measurement of depression. In 2017 International Conference on Information and Communication Technology Convergence (ICTC), pages 138–140.
Islam, M. R., Kamal, A. R. M., Sultana, N., Islam, R., Moni, M. A., and ulhaq, A. (2018). Detecting depression using k-nearest neighbors (knn) classification technique. In 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), pages 1–4.
Keumhee Kang, Chanhee Yoon, and Eun Yi Kim (2016). Identifying depressive users in twitter using multimodal analysis. In 2016 International Conference on Big Data and Smart Computing (BigComp), pages 231–238.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1):1–167.
Ma, L., Wang, Z., and Zhang, Y. (2017). Extracting depression symptoms from social networks and web blogs via text mining. pages 325–330. Springer International Publishing.
Organization, W. H. et al. (2017). Depression and other common mental disorders: global health estimates. Technical report, World Health Organization.
Rosa, R. L., Schwartz, G. M., Ruggiero, W. V., and Rodríguez, D. Z. (2019). A knowledge-based recommendation system that includes sentiment analysis and deep learning. IEEE Transactions on Industrial Informatics, 15(4):2124–2135.
Publicado
29/11/2021
Como Citar
CASANI, Vinicius; SOUZA, Alinne C. Correa; MANTOVANI, Rafael G.; SOUZA, Francisco Carlos M..
DP-Symptom-Identifier: uma estratégia para classificar sintomas de depressão utilizando um conjunto de dados textuais na língua portuguesa. In: SIMPÓSIO BRASILEIRO DE TECNOLOGIA DA INFORMAÇÃO E DA LINGUAGEM HUMANA (STIL), 13. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 153-161.
DOI: https://doi.org/10.5753/stil.2021.17794.
