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Suicidal tendencies and attitude towards freedom to choose suicide among Lithuanian schoolchildren: results from three cross-sectional studies in 1994, 1998, and 2002
Nida Zemaitiene, Apolinaras Zaborskis
BMC Public Health , 2005, DOI: 10.1186/1471-2458-5-83
Abstract: Three country representative samples of schoolchildren, aged 11, 13 and 15, were surveyed in 1994 (n = 5428), 1998 (n = 4513), and 2002 (n = 5645) anonymously in conformity with the methodology of the World Health Organization Cross – National study on Health Behaviour in School-aged Children (HBSC).About one third of respondents reported about suicidal ideation, plans or attempts to commit suicide. In the study period of eight years, the percentage of adolescents who reported sometime suicidal ideation decreased but the percentage of adolescents who declared serious suicidal behaviour remained on the same high level (8.1%, 9.8% and 8.4% correspondingly in 1994, 1998 and 2002). Moreover, the number of suicidal attempts changed from 1.0% in 1994 to 1.8% in the year 1998 and to 1,7% in the year 2002. The schoolchildren's attitude towards suicide became more agreeable: 36.6%, 41.9% and 62.5% of respondents, correspondingly in 1994, 1998 and 2002, answered that they agree with a person's freedom to make a choice between life and suicide. A multiple logistic regression analysis with low level of suicidality and high level of suicidality versus non suicidal behaviour as dependent variables for gender, age, year of the survey and attitude towards freedom to choose suicide as independent variables approved a significant association between studied covariates over the entire study period.Suicidal tendencies are quite frequent among Lithuanian adolescents. An increasing number of schoolchildren are expressing an agreeable attitude towards suicide. The approving attitude towards suicide among adolescents correlates with suicidal ideation and behaviour.Suicidal behaviour is becoming a phenomenon increasingly associated with young people. The rise in the overall suicide rates in many countries is, to a large extent, due to the increase in suicides in the younger age groups. Lithuania has been among the countries with the highest suicide rate for more than ten years. It's extreme
Combining Lexico-semantic Features for Emotion Classification in Suicide Notes
Bart Desmet, and Véronique Hoste
Biomedical Informatics Insights , 2012, DOI: 10.4137/BII.S8960
Abstract: This paper describes a system for automatic emotion classification, developed for the 2011 i2b2 Natural Language Processing Challenge, Track 2. The objective of the shared task was to label suicide notes with 15 relevant emotions on the sentence level. Our system uses 15 SVM models (one for each emotion) using the combination of features that was found to perform best on a given emotion. Features included lemmas and trigram bag of words, and information from semantic resources such as WordNet, SentiWordNet and subjectivity clues. The best-performing system labeled 7 of the 15 emotions and achieved an F-score of 53.31% on the test data.
A Hybrid Model for Automatic Emotion Recognition in Suicide Notes
Hui Yang, Alistair Willis, Anne de Roeck and Bashar Nuseibeh
Biomedical Informatics Insights , 2012, DOI: 10.4137/BII.S8948
Abstract: We describe the Open University team's submission to the 2011 i2b2/VA/Cincinnati Medical Natural Language Processing Challenge, Track 2 Shared Task for sentiment analysis in suicide notes. This Shared Task focused on the development of automatic systems that identify, at the sentence level, affective text of 15 specific emotions from suicide notes. We propose a hybrid model that incorporates a number of natural language processing techniques, including lexicon-based keyword spotting, CRF-based emotion cue identification, and machine learning-based emotion classification. The results generated by different techniques are integrated using different vote-based merging strategies. The automated system performed well against the manually-annotated gold standard, and achieved encouraging results with a micro-averaged F-measure score of 61.39% in textual emotion recognition, which was ranked 1st place out of 24 participant teams in this challenge. The results demonstrate that effective emotion recognition by an automated system is possible when a large annotated corpus is available.
Emotion Detection in Suicide Notes using Maximum Entropy Classification
Richard Wicentowski and Matthew R. Sydes
Biomedical Informatics Insights , 2012, DOI: 10.4137/BII.S8972
Abstract: 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.
Statistical and Similarity Methods for Classifying Emotion in Suicide Notes
Kirk Roberts and Sanda M. Harabagiu
Biomedical Informatics Insights , 2012, DOI: 10.4137/BII.S8958
Abstract: In this paper we report on the approaches that we developed for the 2011 i2b2 Shared Task on Sentiment Analysis of Suicide Notes. We have cast the problem of detecting emotions in suicide notes as a supervised multi-label classification problem. Our classifiers use a variety of features based on (a) lexical indicators, (b) topic scores, and (c) similarity measures. Our best submission has a precision of 0.551, a recall of 0.485, and a F-measure of 0.516.
A Hybrid System for Emotion Extraction from Suicide Notes
Azadeh Nikfarjam, Ehsan Emadzadeh and Graciela Gonzalez
Biomedical Informatics Insights , 2012, DOI: 10.4137/BII.S8981
Abstract: Abstract: The reasons that drive someone to commit suicide are complex and their study has attracted the attention of scientists in different domains. Analyzing this phenomenon could significantly improve the preventive efforts. In this paper we present a method for sentiment analysis of suicide notes submitted to the i2b2/VA/Cincinnati Shared Task 2011. In this task the sentences of 900 suicide notes were labeled with the possible emotions that they reflect. In order to label the sentence with emotions, we propose a hybrid approach which utilizes both rule based and machine learning techniques. To solve the multi class problem a rule-based engine and an SVM model is used for each category. A set of syntactic and semantic features are selected for each sentence to build the rules and train the classifier. The rules are generated manually based on a set of lexical and emotional clues. We propose a new approach to extract the sentence's clauses and constitutive grammatical elements and to use them in syntactic and semantic feature generation. The method utilizes a novel method to measure the polarity of the sentence based on the extracted grammatical elements, reaching precision of 41.79 with recall of 55.03 for an f-measure of 47.50. The overall mean f-measure of all submissions was 48.75% with a standard deviation of 7%.
Binary Classifiers and Latent Sequence Models for Emotion Detection in Suicide Notes
Colin Cherry, Saif M. Mohammad and Berry de Bruijn
Biomedical Informatics Insights , 2012, DOI: 10.4137/BII.S8933
Abstract: 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.
A Combined Approach to Emotion Detection in Suicide Notes
Alexander Pak, Delphine Bernhard, Patrick Paroubek and Cyril Grouin
Biomedical Informatics Insights , 2012, DOI: 10.4137/BII.S8969
Abstract: 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.
Differences between children and adolescents who commit suicide and their peers: A psychological autopsy of suicide victims compared to accident victims and a community sample  [cached]
Freuchen Anne,Kjelsberg Ellen,Lundervold Astri J,Gr?holt Berit
Child and Adolescent Psychiatry and Mental Health , 2012, DOI: 10.1186/1753-2000-6-1
Abstract: Background The purpose of this study was to gain knowledge about the circumstances related to suicide among children and adolescents 15 years and younger. Methods We conducted a psychological autopsy, collecting information from parents, hospital records and police reports on persons below the age of 16 who had committed suicide in Norway during a 12-year period (1993-2004) (n = 41). Those who committed suicide were compared with children and adolescents who were killed in accidents during the same time period (n = 43) and with a community sample. Results: Among the suicides 25% met the criteria for a psychiatric diagnosis and 30% had depressive symptoms at the time of death. Furthermore, 60% of the parents of the suicide victims reported the child experienced some kind of stressful conflict prior to death, whereas only 12% of the parents of the accident victims reported such conflicts. Conclusion One in four suicide victims fulfilled the criteria for a psychiatric diagnosis. The level of sub-threshold depression and of stressful conflict experienced by youths who committed suicide did not appear to differ substantially from that of their peers, and therefore did not raise sufficient concern for referral to professional help.
The young and suicide: An analysis of the attitude to suicide in secondary schools in Subotica  [PDF]
Tokai Monika
Zbornik Matice Srpske za Drustvene Nauke , 2004, DOI: 10.2298/zmsdn0417253t
Abstract: Suicide has always been present in all societies regardless of their religion, governmental structure or culture. This phenomenon that follows the whole history of human kind requires a continuous research in all times and places. This paper tries to contribute to the sociological view of suicidalness, to give insight into some social, demographic and cultural characteristics of suicidal personality. This paper is based on the questionnaire research that was carried out among high school pupils in Subotica. The questionnaire investigates the existence of symptoms of depression among the young, also the demographic and cultural characteristics of depressive ones. The aim of this paper is: – determining the existence of depression among the young according to the symptoms that were fixed by the International Organization of Diseases – determining the level of social integration of the young in correlation with the level of depression, – determining the presence of depression depending on the socio-demographic characteristics, – the analysis of suicides in North Ba ka county.
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