An Explainable Approach Based on Emotion and Sentiment Features for Detecting People with Mental Disorders on Social Networks

Mental disorders are a global problem that widely affects different segments of the population. Diagnosis and treatment are difficult to obtain, as there are not enough specialists on the matter, and mental health is not yet a common topic among the population. The computer science field has propose...

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Autores principales: Leslie Marjorie Gallegos Salazar, Octavio Loyola-González, Miguel Angel Medina-Pérez
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Publicado: MDPI AG 2021
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spelling oai:doaj.org-article:6e0cd10cfb1a43de9a39636a3714fa432021-11-25T16:41:19ZAn Explainable Approach Based on Emotion and Sentiment Features for Detecting People with Mental Disorders on Social Networks10.3390/app1122109322076-3417https://doaj.org/article/6e0cd10cfb1a43de9a39636a3714fa432021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10932https://doaj.org/toc/2076-3417Mental disorders are a global problem that widely affects different segments of the population. Diagnosis and treatment are difficult to obtain, as there are not enough specialists on the matter, and mental health is not yet a common topic among the population. The computer science field has proposed some solutions to detect the risk of depression, based on language use and data obtained through social media. These solutions are mainly focused on objective features, such as n-grams and lexicons, which are complicated to be understood by experts in the application area. Hence, in this paper, we propose a contrast pattern-based classifier to detect depression by using a new data representation based only on emotion and sentiment analysis extracted from posts on social media. Our proposed feature representation contains 28 different features, which are more understandable by specialists than other proposed representations. Our feature representation jointly with a contrast pattern-based classifier has obtained better classification results than five other combinations of features and classifiers reported in the literature. Our proposal statistically outperformed the Random Forest, Naive Bayes, and AdaBoost classifiers using the parser-tree, VAD (Valence, Arousal, and Dominance) and Topics, and Bag of Words (BOW) representations. It obtained similar statistical results to the logistic regression models using the Ensemble of BOWs and Handcrafted features representations. In all cases, our proposal was able to provide an explanation close to the language of experts, due to the mined contrast patterns.Leslie Marjorie Gallegos SalazarOctavio Loyola-GonzálezMiguel Angel Medina-PérezMDPI AGarticledepression detectionsocial medianatural language processingTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10932, p 10932 (2021)
institution DOAJ
collection DOAJ
language EN
topic depression detection
social media
natural language processing
Technology
T
Engineering (General). Civil engineering (General)
TA1-2040
Biology (General)
QH301-705.5
Physics
QC1-999
Chemistry
QD1-999
spellingShingle depression detection
social media
natural language processing
Technology
T
Engineering (General). Civil engineering (General)
TA1-2040
Biology (General)
QH301-705.5
Physics
QC1-999
Chemistry
QD1-999
Leslie Marjorie Gallegos Salazar
Octavio Loyola-González
Miguel Angel Medina-Pérez
An Explainable Approach Based on Emotion and Sentiment Features for Detecting People with Mental Disorders on Social Networks
description Mental disorders are a global problem that widely affects different segments of the population. Diagnosis and treatment are difficult to obtain, as there are not enough specialists on the matter, and mental health is not yet a common topic among the population. The computer science field has proposed some solutions to detect the risk of depression, based on language use and data obtained through social media. These solutions are mainly focused on objective features, such as n-grams and lexicons, which are complicated to be understood by experts in the application area. Hence, in this paper, we propose a contrast pattern-based classifier to detect depression by using a new data representation based only on emotion and sentiment analysis extracted from posts on social media. Our proposed feature representation contains 28 different features, which are more understandable by specialists than other proposed representations. Our feature representation jointly with a contrast pattern-based classifier has obtained better classification results than five other combinations of features and classifiers reported in the literature. Our proposal statistically outperformed the Random Forest, Naive Bayes, and AdaBoost classifiers using the parser-tree, VAD (Valence, Arousal, and Dominance) and Topics, and Bag of Words (BOW) representations. It obtained similar statistical results to the logistic regression models using the Ensemble of BOWs and Handcrafted features representations. In all cases, our proposal was able to provide an explanation close to the language of experts, due to the mined contrast patterns.
format article
author Leslie Marjorie Gallegos Salazar
Octavio Loyola-González
Miguel Angel Medina-Pérez
author_facet Leslie Marjorie Gallegos Salazar
Octavio Loyola-González
Miguel Angel Medina-Pérez
author_sort Leslie Marjorie Gallegos Salazar
title An Explainable Approach Based on Emotion and Sentiment Features for Detecting People with Mental Disorders on Social Networks
title_short An Explainable Approach Based on Emotion and Sentiment Features for Detecting People with Mental Disorders on Social Networks
title_full An Explainable Approach Based on Emotion and Sentiment Features for Detecting People with Mental Disorders on Social Networks
title_fullStr An Explainable Approach Based on Emotion and Sentiment Features for Detecting People with Mental Disorders on Social Networks
title_full_unstemmed An Explainable Approach Based on Emotion and Sentiment Features for Detecting People with Mental Disorders on Social Networks
title_sort explainable approach based on emotion and sentiment features for detecting people with mental disorders on social networks
publisher MDPI AG
publishDate 2021
url https://doaj.org/article/6e0cd10cfb1a43de9a39636a3714fa43
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