Detección de ansiedad mediante minería de texto en la era de redes sociales: Revisión bibliográfica

Valeria Torres; Orlando Erazo

doi:10.29057/esh.v11i22.10879

Valeria Torres Universidad Técnica Estatal de Quevedo https://orcid.org/0000-0001-8778-2794
Orlando Erazo Universidad Técnica Estatal de Quevedo https://orcid.org/0000-0001-5642-9920

DOI: https://doi.org/10.29057/esh.v11i22.10879

Palabras clave: Ansiedad, minería de texto, redes sociales, aprendizaje automático

Resumen

Los trastornos mentales son cada vez más comunes, principalmente la ansiedad. Este trastorno, al no ser detectado a tiempo, puede volverse algo grave llevando a extremos como el suicidio. Sin embargo, dado que varias personas que lo padecen optan por la interacción en línea, existe la posibilidad de recurrir a la minería de texto enfocada a redes sociales. En este sentido, con el presente trabajo se buscó revisar la bibliografía que reporte estudios empleando minería de texto para determinar los usuarios que padecían de ansiedad mediante sus publicaciones o comentarios en sus redes sociales. La revisión se organizó a partir de las fases de minería de texto; es decir, recopilación de datos, preparación o preprocesamiento y clasificación. Entre los aspectos a resaltar están (i) la tendencia a utilizar una red social para obtener datos, especialmente Twitter; (ii) la relevancia de la limpieza de datos, aplicando técnicas como lemmatization; (iii) los algoritmos más destacados en la detección de ansiedad, como Naive Bayes, regresión logística, SVM y random forest. Más allá de los aportes de los trabajos revisados, se puede notar que persiste la necesidad de desarrollar más modelos que detecten el trastorno de interés.

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