Emotion Detection in Suicide Notes using Maximum Entropy Classification

Wicentowski, Richard; Sydes, Matthew R.

doi:10.4137/bii.s8972

Cited by 18 publications

(17 citation statements)

References 6 publications

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“…NLP can further distinguish between genuine and simulated suicide notes at a higher rate than mental healthcare practitioners (Pestian et al, 2008(Pestian et al, , 2010. Algorithms have also been designed to automate emotion detection in suicide notes via affective computing (Cherry et al, 2012;Wicentowski and Sydes, 2012) and assist with classifying content into specific themes (Spasic et al, 2012).…”

Section: Role Of Ai In Suicide Risk Predictionmentioning

confidence: 99%

The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors

Fonseka

Bhat

Kennedy

2019

Aust N Z J Psychiatry

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Objective: Suicide is a growing public health concern with a global prevalence of approximately 800,000 deaths per year. The current process of evaluating suicide risk is highly subjective, which can limit the efficacy and accuracy of prediction efforts. Consequently, suicide detection strategies are shifting toward artificial intelligence platforms that can identify patterns within ‘big data’ to generate risk algorithms that can determine the effects of risk (and protective) factors on suicide outcomes, predict suicide outbreaks and identify at-risk individuals or populations. In this review, we summarize the role of artificial intelligence in optimizing suicide risk prediction and behavior management. Methods: This paper provides a general review of the literature. A literature search was conducted in OVID Medline, EMBASE and PsycINFO databases with coverage from January 1990 to June 2019. Results were restricted to peer-reviewed, English-language articles. Conference and dissertation proceedings, case reports, protocol papers and opinion pieces were excluded. Reference lists were also examined for additional articles of relevance. Results: At the individual level, prediction analytics help to identify individuals in crisis to intervene with emotional support, crisis and psychoeducational resources, and alerts for emergency assistance. At the population level, algorithms can identify at-risk groups or suicide hotspots, which help inform resource mobilization, policy reform and advocacy efforts. Artificial intelligence has also been used to support the clinical management of suicide across diagnostics and evaluation, medication management and behavioral therapy delivery. There could be several advantages of incorporating artificial intelligence into suicide care, which includes a time- and resource-effective alternative to clinician-based strategies, adaptability to various settings and demographics, and suitability for use in remote locations with limited access to mental healthcare supports. Conclusion: Based on the observed benefits to date, artificial intelligence has a demonstrated utility within suicide prediction and clinical management efforts and will continue to advance mental healthcare forward.

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Section: Role Of Ai In Suicide Risk Predictionmentioning

confidence: 99%

The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors

Fonseka

Bhat

Kennedy

2019

Aust N Z J Psychiatry

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“…NLP challenge proposed by Informatics for Integrating Biology and Bedside (i2b2) in 2011 dealt with SA of suicide notes [16]. Many researchers participated in this challenge and the results were published in several papers [3,5,15,[17][18][19][20][21][22][23]. This challenge provided researchers with 900 notes made by those who already committed suicide and died as a result.…”

Section: -Resultsmentioning

confidence: 99%

“…Those with a failed attempt of suicide are at a high risk of making a new suicide attempt (12-30%) or completing suicide (1-3%) within the next one year [2]. An analysis of suicide notes can partly help to delve into their mind [3,5,9,14,15,[18][19][20][21][22][23]. Even in an absence of any notes, questionnaires can be used to explore their emotions [4,24].…”

Section: -Discussionmentioning

confidence: 99%

Using of Natural Language Processing Techniques in Suicide Research

Orooji

Langarizadeh

2017

Emerg Sci J

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It is estimated that each year many people, most of whom are teenagers and young adults die by suicide worldwide. Suicide receives special attention with many countries developing national strategies for prevention. Since, more medical information is available in text, Preventing the growing trend of suicide in communities requires analyzing various textual resources, such as patient records, information on the web or questionnaires. For this purpose, this study systematically reviews recent studies related to the use of natural language processing techniques in the area of people’s health who have completed suicide or are at risk. After electronically searching for the PubMed and ScienceDirect databases and studying articles by two reviewers, 21 articles matched the inclusion criteria. This study revealed that, if a suitable data set is available, natural language processing techniques are well suited for various types of suicide related research.

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“…Esto abre una ventana de desafíos y retos para llevar a cabo procesamiento automático de textos en español. Además, algunos trabajos revisados (Reyes-Ortiz & Bravo, 2018;Sawhney et al, 2018;Wicentowski & Sydes, 2012;Luyckx et al, 2012;Liakata et al, 2012;Poulin et al, 2014;Carson et al, 2019), se enfocan, solamente, en la identificación o clasificación del suicidio, mientras que nuestro trabajo añade la extracción y análisis de las causas del mismo. Por lo tanto, además de aportar una solución al problema de la extracción automática de causas del suicidio, brinda un panorama sobre los marcadores lingüísticos causales que son característicos de este tipo de textos en español y almacena las causas del suicidio reportadas en notas periodísticas.…”

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“…Existen trabajos que han analizado las notas clínicas para predecir los riesgos de suicidios en los pacientes, como en el trabajode Poulin et al (2014) que utiliza un algoritmo de aprendizaje automático basado en programación genética para llevar a cabo esta tarea a partir de notas clínicas en inglés. El análisis de notas suicidas mediante técnicas de minería de sentimientos es un tema que ayuda en la prevención del suicidio, en la cual se han detectado trabajos que identifican de manera automática emociones (culpa, felicidad, agradecimiento, amor, información, desesperanza e instrucciones) en estas notas usando características de los textos con algoritmos de aprendizaje automático tales como: máquinas de soporte vectorial(Desmet & Hoste, 2013; Luyckx et al, 2012), campos aleatorios condicionales(Liakata et al, 2012) y un clasificador de máxima entropía(Wicentowski & Sydes, 2012). El aspecto lingüístico en el suicidio es de gran importancia.…”

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Extracción y análisis de las causas de suicidio a través de marcadores lingüísticos en reportes periodísticos

Ortiz¹,

Tovar²

2020

Linguamática

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El análisis automático de información(textos) sobre el suicidio se ha convertido en un reto para el campo de investigación en lingüística computacional, cada vez más, son necesarias herramientas que ayuden a disminuir las tasas de suicidios, por ejemplo, extraer las causas para apoyar en su detección temprana. Los aspectos lingüísticos en los textos en Español, tales como frases clave o partes de la oración, pueden ayudar en dicha tarea. Por ello, en este artículo se presenta un enfoque computacional para la extracción y análisis de causas a partir de cabeceras de reportes periodísticos sobre el suicidio en español. La tarea de extracción automática de causas de suicidio es llevada a cabo mediante marcadores lingüísticos basados en verbos, conectores, preposiciones y conjunciones. Por su parte, el análisis de las causas de suicidio es realizado en dos enfoques: a) un análisis centrado en frases verbales y nominales, estudiando la presencia de la negación; b) un análisis centrado en la frecuencia de los unigramas y bigramas de palabras. Ambos análisis muestran resultados prometedores, los cuales son útiles para conocer los motivos de los suicidios reportados en México en un periodo determinado. Finalmente, se obtiene una colección de 581 causas del suicidio.

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Emotion Detection in Suicide Notes using Maximum Entropy Classification

Cited by 18 publications

References 6 publications

The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors

The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors

Using of Natural Language Processing Techniques in Suicide Research

Extracción y análisis de las causas de suicidio a través de marcadores lingüísticos en reportes periodísticos

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