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
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	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
Acceso en línea:	https://doaj.org/article/6e0cd10cfb1a43de9a39636a3714fa43
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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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An Explainable Approach Based on Emotion and Sentiment Features for Detecting People with Mental Disorders on Social Networks

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