Deep Learning Algorithm for Suicide Sentiment Prediction

Boukil, Samir; Adnani, Fatiha El; Cherrat, Loubna; Moutaouakkil, Abd Elmajid El; Ezziyyani, Mostafa

doi:10.1007/978-3-030-11884-6_24

Cited by 10 publications

(7 citation statements)

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“…Os usuários foram extraídos do conjunto de 180 usuários (90 depressivos e 90 não depressivos) que foram detectados por um grupo de psicólogos em [30]. [24] IBK, J48, SMO; PART Twitter Automática - [25] PLN; SVM; Random Forest Reddit Manual - [26] PLN juntamente com Lexicos Fórum de discussão PTT Manual - [27] CNN; NB; KNN Notas escritas em Árabe Manual - [28] PLN juntamente com LDA Twitter e blogs de depressão Automática - [29] SVM; LIWC Twitter Manual -…”

Section: Resultados E Discussõesunclassified

“…Aldarwish e Ahmad [12] classificaram 2073 postagens como deprimidas e 4700 como não deprimidas, após isso as postagens A base de dados do estudo de Boukil et. al [27] foi criado a partir de postagens escritas em árabe, coletadas de diversos sites (Facebbok, Twitter, blogs e fóruns). Todos os dados coletados foram manualmente analisados e rotulados nas seguintes classes: notas com indicação suicida e notas sem indicação suicida.…”

Section: Figure 4: Técnicas Identificadas Nos Estudos Primáriosunclassified

“…Notas em árabe foram utilizados por Boukil et. al [27] para compor sua base de dados, a justificativa da escolha desta fonte de dados foi a dificuldade de encontrar informações referentes a depressão e ao suicido escritas em árabe, já que em países árabes o tabu referente a estes problemas é maior que no restante do mundo. O objetivo dos autores é criar um sistema automático para extrair e prever a intenção de suicídio de um indivíduo, com base em mensagens e notas textuais do mesmo.…”

Section: Análise E Discussãounclassified

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Identificação de Perfis Depressivos em Redes Sociais Utilizando Aprendizado de Máquina: um Mapeamento Sistemático

Casani¹,

Mantovani²,

Souza³

et al. 2020

Anais Do XI Computer on the Beach - COTB '20

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Depression is a psychological disorder that affects millions of peoplein theworld, regardless of their age, social class or nationality. In theliterature, different techniques have been studying to analyze andrecognize this disease such as Natural Language Processing, SentimentAnalysis, and Machine Learning. In this paper, we describe asystematic mapping to identify evidence regarding techniques thatare often used to identify depressive profiles.We analyzed 472 studiesand we selected 25 primary studies. These studies indicate thatthe SVM and NB techniques have been most used to detect possibledepressive profiles in social networks. Furthermore, Twitter andFacebook with 35,5% and 22,6%, respectively were the social mediamost have been used by users’ express their feelings regarding themost varied subjects.

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Section: Resultados E Discussõesunclassified

Section: Figure 4: Técnicas Identificadas Nos Estudos Primáriosunclassified

Section: Análise E Discussãounclassified

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Identificação de Perfis Depressivos em Redes Sociais Utilizando Aprendizado de Máquina: um Mapeamento Sistemático

Casani¹,

Mantovani²,

Souza³

et al. 2020

Anais Do XI Computer on the Beach - COTB '20

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“…More importantly, our study, similar to ample research, suggested that the model based on linguistic features for predicting psychological states may become increasingly feasible and more accurate. For example, Boukil et al (2019) proposed an automatic system based on Deep learning algorithm to predict suicidal ideation through analyzing sentiment and feelings expressed on social media. Similarly, Nguyen et al (2017) extract linguistic features and topics to discriminate depressed online communities from other groups based on machine learning algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…First, psychological states are assessed by the way people write about their experiences ( Barnes et al, 2007 ; Tausczik and Pennebaker, 2010 ; Al-Mosaiwi and Johnstone, 2018 ; Kim et al, 2019 ). Second, psychological states are predicted by factors such as the diachronic changes of linguistic features in written texts ( Rodrigues et al, 2016 ; Ziemer and Korkmaz, 2017 ; Eichstaedt et al, 2018 ; Boukil et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Linguistic Features and Psychological States: The Case of Virginia Woolf

2022

Front. Psychol.

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This study investigated the relation between psychological states and linguistic features with the case of Virginia Woolf. We analyzed the data from The Diary of Virginia Woolf and Virginia Woolf: Biography by automatic text analysis and statistical analysis, including stepwise multiple regression and Deep Learning algorithm. The results suggested that the significant linguistic features can jointly predict the psychological states of Virginia Woolf, including the emotional value of anger, the absolutist word “everything,” and the total of first-person plural pronouns. In addition, we found that the total use of first-person plural pronouns and the emotional value of anger were negatively related to mental health of Virginia Woolf. While the use of the absolutist word “everything” was positively related to mental health of Virginia Woolf. Meanwhile, we developed a model that can predict the psychological states of Virginia Woolf, with 86.9% accuracy. We discussed the findings and enumerated the limitations of this study at the end of the paper. The results not only complemented previous studies in the understanding of the relation between language and psychological health, but also facilitated timely identification, intervention, and prevention of mental disorders.

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