On the assessment of personality traits by using text mining techniques

This paper reports a complete experience of the Knowledge Discovery in Databases process to solve a personality trait assessment problem using text mining techniques. Given that this work is part of an interdisciplinary study between researchers from the fields of Computer Science and Psychology, in...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Montenegro, Luis, Sapino, Maximiliano, Ferretti, Edgardo, Cagnina, Leticia
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2023
Materias:
Ciencias Informáticas
Personality Traits
Big Five Factors
Knowledge Discovery in Databases
Text Mining
Data Augmentation
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/164884
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-164884
record_format dspace
spelling I19-R120-10915-1648842024-04-16T20:02:58Z http://sedici.unlp.edu.ar/handle/10915/164884 On the assessment of personality traits by using text mining techniques Montenegro, Luis Sapino, Maximiliano Ferretti, Edgardo Cagnina, Leticia 2023-10 2024 2024-04-16T14:51:07Z en Ciencias Informáticas Personality Traits Big Five Factors Knowledge Discovery in Databases Text Mining Data Augmentation This paper reports a complete experience of the Knowledge Discovery in Databases process to solve a personality trait assessment problem using text mining techniques. Given that this work is part of an interdisciplinary study between researchers from the fields of Computer Science and Psychology, in this first approach, four simple predictive algorithms were evaluated; namely: Multinomial Naive Bayes, Logistic Regression, Support Vector Machines and Decision Trees. Moreover, given the nature of the problem faced, where one person may present more than one personality trait, but not necessary all of them, it was modeled by three different classification tasks: viz. binary, multiclass and multilabel. Besides, data augmentation was used as a useful technique to improve the performance of all the classification approaches evaluated. Particularly, binary classification was the approach which took more advantage of using this technique by improving its performance on average by 13% compared to the original dataset. For three out of the five personality traits studied, it achieves weighted-F1 scores above 0.75 and in particular the highest score of 0.88 was achieved for the Responsibility trait. Red de Universidades con Carreras en Informática Objeto de conferencia Objeto de conferencia http://creativecommons.org/licenses/by-nc-sa/4.0/ Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) application/pdf 114-123
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Personality Traits
Big Five Factors
Knowledge Discovery in Databases
Text Mining
Data Augmentation
spellingShingle Ciencias Informáticas
Personality Traits
Big Five Factors
Knowledge Discovery in Databases
Text Mining
Data Augmentation
Montenegro, Luis
Sapino, Maximiliano
Ferretti, Edgardo
Cagnina, Leticia
On the assessment of personality traits by using text mining techniques
topic_facet Ciencias Informáticas
Personality Traits
Big Five Factors
Knowledge Discovery in Databases
Text Mining
Data Augmentation
description This paper reports a complete experience of the Knowledge Discovery in Databases process to solve a personality trait assessment problem using text mining techniques. Given that this work is part of an interdisciplinary study between researchers from the fields of Computer Science and Psychology, in this first approach, four simple predictive algorithms were evaluated; namely: Multinomial Naive Bayes, Logistic Regression, Support Vector Machines and Decision Trees. Moreover, given the nature of the problem faced, where one person may present more than one personality trait, but not necessary all of them, it was modeled by three different classification tasks: viz. binary, multiclass and multilabel. Besides, data augmentation was used as a useful technique to improve the performance of all the classification approaches evaluated. Particularly, binary classification was the approach which took more advantage of using this technique by improving its performance on average by 13% compared to the original dataset. For three out of the five personality traits studied, it achieves weighted-F1 scores above 0.75 and in particular the highest score of 0.88 was achieved for the Responsibility trait.
format Objeto de conferencia
Objeto de conferencia
author Montenegro, Luis
Sapino, Maximiliano
Ferretti, Edgardo
Cagnina, Leticia
author_facet Montenegro, Luis
Sapino, Maximiliano
Ferretti, Edgardo
Cagnina, Leticia
author_sort Montenegro, Luis
title On the assessment of personality traits by using text mining techniques
title_short On the assessment of personality traits by using text mining techniques
title_full On the assessment of personality traits by using text mining techniques
title_fullStr On the assessment of personality traits by using text mining techniques
title_full_unstemmed On the assessment of personality traits by using text mining techniques
title_sort on the assessment of personality traits by using text mining techniques
publishDate 2023
url http://sedici.unlp.edu.ar/handle/10915/164884
work_keys_str_mv AT montenegroluis ontheassessmentofpersonalitytraitsbyusingtextminingtechniques
AT sapinomaximiliano ontheassessmentofpersonalitytraitsbyusingtextminingtechniques
AT ferrettiedgardo ontheassessmentofpersonalitytraitsbyusingtextminingtechniques
AT cagninaleticia ontheassessmentofpersonalitytraitsbyusingtextminingtechniques
_version_ 1807222939302494208

Ejemplares similares