Autor según el artículo: Morales Sanchez, Damian; Moreno, Antonio; Jimenez Lopez, Maria Dolores
Departamento: Enginyeria Informàtica i Matemàtiques Filologies Romàniques
Autor/es de la URV: Jiménez López, María Dolores / Morales Sánchez, Damián / Moreno Ribas, Antonio
Palabras clave: Machine learning Gender detection Computational sociolinguistics Author profiling Author
Resumen: Within the area of Natural Language Processing, we approached the Author Profiling task as a text classification problem. Based on the author's writing style, sociodemographic information, such as the author's gender, age, or native language can be predicted. The exponential growth of user-generated data and the development of Machine-Learning techniques have led to significant advances in automatic gender detection. Unfortunately, gender detection models often become black-boxes in terms of interpretability. In this paper, we propose a tree-based computational model for gender detection made up of 198 features. Unlike the previous works on gender detection, we organized the features from a linguistic perspective into six categories: orthographic, morphological, lexical, syntactic, digital, and pragmatics-discursive. We implemented a Decision-Tree classifier to evaluate the performance of all feature combinations, and the experiments revealed that, on average, the classification accuracy increased up to 3.25% with the addition of feature sets. The maximum classification accuracy was reached by a three-level model that combined lexical, syntactic, and digital features. We present the most relevant features for gender detection according to the trees generated by the classifier and contextualize the significance of the computational results with the linguistic patterns defined by previous research in relation to gender.
Áreas temáticas: Química Process chemistry and technology Physics, applied Materials science, multidisciplinary Materials science (miscellaneous) Materials science (all) Materiais Instrumentation General materials science General engineering Fluid flow and transfer processes Engineering, multidisciplinary Engineering (miscellaneous) Engineering (all) Engenharias ii Engenharias i Computer science applications Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência de alimentos Chemistry, multidisciplinary Biodiversidade Astronomia / física
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: damian.morales@urv.cat damian.morales@urv.cat antonio.moreno@urv.cat mariadolores.jimenez@urv.cat
Identificador del autor: 0000-0003-3945-2314 0000-0001-5544-3210
Fecha de alta del registro: 2024-10-12
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.mdpi.com/2076-3417/12/5/2676
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Applied Sciences-Basel. 12 (5): 2676-
Referencia de l'ítem segons les normes APA: Morales Sanchez, Damian; Moreno, Antonio; Jimenez Lopez, Maria Dolores (2022). A White-Box Sociolinguistic Model for Gender Detection. Applied Sciences-Basel, 12(5), 2676-. DOI: 10.3390/app12052676
DOI del artículo: 10.3390/app12052676
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2022
Tipo de publicación: Journal Publications