Emotion detection in suicide notes

2013 ◽  
Vol 40 (16) ◽  
pp. 6351-6358 ◽  
Author(s):  
Bart Desmet ◽  
Véronique Hoste
Keyword(s):  
Emotion Detection ◽  
Suicide Notes

scholarly journals LASSA: Emotion Detection via Information Fusion

2012 ◽  
Vol 5s1 ◽  
pp. BII.S8949 ◽  
Author(s):  
Ning Yu ◽  
Sandra Kübler ◽  
Joshua Herring ◽  
Yu-Yin Hsu ◽  
Ross Israel ◽  
...  
Keyword(s):  
Information Fusion ◽  
Sentiment Classification ◽  
Emotion Detection ◽  
Linguistic Features ◽  
Future Directions ◽  
Suicide Notes ◽  
Fusion Approach

Due to the complexity of emotions in suicide notes and the subtle nature of sentiments, this study proposes a fusion approach to tackle the challenge of sentiment classification in suicide notes: leveraging WordNet-based lexicons, manually created rules, character-based n-grams, and other linguistic features. Although our results are not satisfying, some valuable lessons are learned and promising future directions are identified.

scholarly journals A Combined Approach to Emotion Detection in Suicide Notes

2012 ◽  
Vol 5s1 ◽  
pp. BII.S8969 ◽  
Author(s):  
Alexander Pak ◽  
Delphine Bernhard ◽  
Patrick Paroubek ◽  
Cyril Grouin
Keyword(s):  
Machine Learning ◽  
The Other ◽  
Combined Approach ◽  
Emotion Detection ◽  
Suicide Notes ◽  
Clinical Records ◽  
Official Evaluation ◽  
F Measure

In this paper, we present the system we have developed for participating in the second task of the i2b2/VA 2011 challenge dedicated to emotion detection in clinical records. On the official evaluation, we ranked 6th out of 26 participants. Our best configuration, based upon a combination of both a machine-learning based approach and manually-defined transducers, obtained a 0.5383 global F-measure, while the distribution of the other 26 participants’ results is characterized by mean = 0.4875, stdev = 0.0742, min = 0.2967, max = 0.6139, and median = 0.5027. Combination of machine learning and transducer is achieved by computing the union of results from both approaches, each using a hierarchy of sentiment specific classifiers.

scholarly journals Fine-Grained Emotion Detection in Suicide Notes: A Thresholding Approach to Multi-Label Classification

2012 ◽  
Vol 5s1 ◽  
pp. BII.S8966 ◽  
Author(s):  
Kim Luyckx ◽  
Frederik Vaassen ◽  
Claudia Peersman ◽  
Walter Daelemans
Keyword(s):  
Test Sentence ◽  
Svm Classifier ◽  
Emotion Detection ◽  
Fine Grained ◽  
Suicide Notes ◽  
Probability Estimates ◽  
Single Instance ◽  
Emotion Labels

We present a system to automatically identify emotion-carrying sentences in suicide notes and to detect the specific fine-grained emotion conveyed. With this system, we competed in Track 2 of the 2011 Medical NLP Challenge, 14 where the task was to distinguish between fifteen emotion labels, from guilt, sorrow, and hopelessness to hopefulness and happiness. Since a sentence can be annotated with multiple emotions, we designed a thresholding approach that enables assigning multiple labels to a single instance. We rely on the probability estimates returned by an SVM classifier and experimentally set thresholds on these probabilities. Emotion labels are assigned only if their probability exceeds a certain threshold and if the probability of the sentence being emotion-free is low enough. We show the advantages of this thresholding approach by comparing it to a naïve system that assigns only the most probable label to each test sentence, and to a system trained on emotion-carrying sentences only.

scholarly journals Emotion Detection in Suicide Notes using Maximum Entropy Classification

2012 ◽  
Vol 5s1 ◽  
pp. BII.S8972 ◽  
Author(s):  
Richard Wicentowski ◽  
Matthew R. Sydes
Keyword(s):  
Maximum Entropy ◽  
Test Data ◽  
Training Data ◽  
Emotion Detection ◽  
Shared Task ◽  
Training Set ◽  
Suicide Notes ◽  
Syntactic Features ◽  
Formed Part

An ensemble of supervised maximum entropy classifiers can accurately detect and identify sentiments expressed in suicide notes. Using lexical and syntactic features extracted from a training set of externally annotated suicide notes, we trained separate classifiers for each of fifteen pre-specified emotions. This formed part of the 2011 i2b2 NLP Shared Task, Track 2. The precision and recall of these classifiers related strongly with the number of occurrences of each emotion in the training data. Evaluating on previously unseen test data, our best system achieved an F1 score of 0.534.

scholarly journals Binary Classifiers and Latent Sequence Models for Emotion Detection in Suicide Notes

2012 ◽  
Vol 5s1 ◽  
pp. BII.S8933 ◽  
Author(s):  
Colin Cherry ◽  
Saif M. Mohammad ◽  
Berry De Bruijn
Keyword(s):  
National Research Council ◽  
Training Data ◽  
Emotion Detection ◽  
Suicide Notes ◽  
Comparable Performance ◽  
Fast Development ◽  
Suicide Note ◽  
Sentence Classification ◽  
Binary Classifiers ◽  
F Measure

This paper describes the National Research Council of Canada's submission to the 2011 i2b2 NLP challenge on the detection of emotions in suicide notes. In this task, each sentence of a suicide note is annotated with zero or more emotions, making it a multi-label sentence classification task. We employ two distinct large-margin models capable of handling multiple labels. The first uses one classifier per emotion, and is built to simplify label balance issues and to allow extremely fast development. This approach is very effective, scoring an F-measure of 55.22 and placing fourth in the competition, making it the best system that does not use web-derived statistics or re-annotated training data. Second, we present a latent sequence model, which learns to segment the sentence into a number of emotion regions. This model is intended to gracefully handle sentences that convey multiple thoughts and emotions. Preliminary work with the latent sequence model shows promise, resulting in comparable performance using fewer features.

Suicide in Nógrád County, Hungary, 1970-1994

Crisis ◽  
1999 ◽  
Vol 20 (2) ◽  
pp. 64-70 ◽  
Author(s):  
Tamás Zonda
Keyword(s):  
High Risk ◽  
Psychiatric Disorders ◽  
Suicide Rate ◽  
Risk Groups ◽  
The Elderly ◽  
Female Ratio ◽  
Suicide Notes ◽  
Suicidal Intention ◽  
Male Female Ratio

The author examined completed suicides occurring over a period of 25 years in a county of Hungary with a traditionally low (relatively speaking) suicide rate of 25.8. The rates are clearly higher in villages than in the towns. The male/female ratio was close to 4:1, among elderly though only 1.5:1. The high risk groups are the elderly, divorced, and widowed. Violent methods are chosen in 66.4% of the cases. The rates are particularly high in the period April-July. Prior communication of suicidal intention was revealed in 16.3% of all cases. Previous attempts had been undertaken by 17%, which in turn means that 83% of suicides were first attempts. In our material 10% the victims left suicide notes. Psychiatric disorders were present in 60.1% of the cases, and severe, multiple somatic illnesses (including malignomas) were present in 8.8%. The majority of the data resemble those found in the literature.

Analyzing Language in Suicide Notes and Legacy Tokens

Crisis ◽  
2016 ◽  
Vol 37 (2) ◽  
pp. 140-147 ◽  
Author(s):  
Michael J. Egnoto ◽  
Darrin J. Griffin
Keyword(s):  
Text Analysis ◽  
Low Cost ◽  
Computer Software ◽  
Student Writing ◽  
Computer Assisted ◽  
Automated Identification ◽  
Completed Suicide ◽  
Suicide Notes ◽  
Self Harm ◽  
Harm To Others

Abstract. Background: Identifying precursors that will aid in the discovery of individuals who may harm themselves or others has long been a focus of scholarly research. Aim: This work set out to determine if it is possible to use the legacy tokens of active shooters and notes left from individuals who completed suicide to uncover signals that foreshadow their behavior. Method: A total of 25 suicide notes and 21 legacy tokens were compared with a sample of over 20,000 student writings for a preliminary computer-assisted text analysis to determine what differences can be coded with existing computer software to better identify students who may commit self-harm or harm to others. Results: The results support that text analysis techniques with the Linguistic Inquiry and Word Count (LIWC) tool are effective for identifying suicidal or homicidal writings as distinct from each other and from a variety of student writings in an automated fashion. Conclusion: Findings indicate support for automated identification of writings that were associated with harm to self, harm to others, and various other student writing products. This work begins to uncover the viability or larger scale, low cost methods of automatic detection for individuals suffering from harmful ideation.

Can a Theory of Suicide Predict All “Suicides” in the Elderly?

Crisis ◽  
2003 ◽  
Vol 24 (1) ◽  
pp. 7-16 ◽  
Author(s):  
Antoon A. Leenaars
Keyword(s):  
Sigmund Freud ◽  
Terminally Ill ◽  
Later Life ◽  
Psychological Theory ◽  
The Elderly ◽  
Theory Building ◽  
Personality Factors ◽  
Comprehensive Model ◽  
Suicide Notes ◽  
Psychiatric Study

Summary: Older adults consistently have the highest rates of suicide in most societies. Despite the paucity of studies until recently, research has shown that suicides in later life are best understood as a multidimensional event. An especially neglected area of research is the psychological/psychiatric study of personality factors in the event. This paper outlines one comprehensive model of suicide and then raises the question: Is such a psychiatric/psychological theory applicable to all suicides in the elderly? To address the question, I discuss the case of Sigmund Freud; raise the topic of suicide and/or dignified death in the terminally ill; and examine suicide notes of the both terminally ill and nonterminally ill elderly. I conclude that, indeed, greater study and theory building are needed into the “suicides” of the elderly, including those who are terminally ill.

A Multidimensional Theory of Suicide

Crisis ◽  
2018 ◽  
Vol 39 (6) ◽  
pp. 416-427 ◽  
Author(s):  
Antoon A. Leenaars ◽  
Gudrun Dieserud ◽  
Susanne Wenckstern ◽  
Kari Dyregrov ◽  
David Lester ◽  
...  
Keyword(s):  
Multidimensional Model ◽  
Psychological Autopsy ◽  
Suicide Survivors ◽  
Background Theory ◽  
Independent Verification ◽  
Suicide Notes ◽  
Interjudge Reliability ◽  
The Relationship ◽  
Depth Interviews ◽  
Deceased People

Abstract. Background: Theory is the foundation of science; this is true in suicidology. Over decades of studies of suicide notes, Leenaars developed a multidimensional model of suicide, with international (crosscultural) studies and independent verification. Aim: To corroborate Leenaars's theory with a psychological autopsy (PA) study, examining age and sex of the decedent, and survivor's relationship to deceased. Method: A PA study in Norway, with 120 survivors/informants was undertaken. Leenaars' theoretical–conceptual (protocol) analysis was undertaken of the survivors' narratives and in-depth interviews combined. Results: Substantial interjudge reliability was noted (κ = .632). Overall, there was considerable confirmatory evidence of Leenaars's intrapsychic and interpersonal factors in suicide survivors' narratives. Differences were found in the age of the decedent, but not in sex, nor in the survivor's closeness of the relationship. Older deceased people were perceived to exhibit more heightened unbearable intrapsychic pain, associated with the suicide. Conclusion: Leenaars's theory has corroborative verification, through the decedents' suicide notes and the survivors' narratives. However, the multidimensional model needs further testing to develop a better evidence-based way of understanding suicide.

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