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Multi-Class Suicide Risk Prediction on Twitter Using Machine Learning Techniques

机译：电机学习技术推特上的多级自杀风险预测

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The enormous growth of Social Networking Sites (SNS) resulted in more virtual engagement of people in the last decade. Amount of data generated through these SNS is enormous, allowing researchers to analyse this Big data. People share their opinions and thoughts related to any topic of interest. As suicide is one the leading cause of death worldwide, it has become a hot topic on which different researchers are working. The Covid19 further amplified the crisis due to social isolation which is the main risk factor for suicide. The problem has usually been analysed and dealt through a physiological point of view using Questionnaires and face to face settings but social stigma prevents its efficacy. In our research, we use well-known machine learning algorithms for multi-classification of Suicidal risk on social media so that individuals having high risk could be identified and counselled properly to save precious human lives. The data has been experimented through four popular machine learning algorithms: Logistic Regression, Multinomial Naïve Bayes, Support Vector Machine and Decision tree. The results generated are impressive with F1 Score ranging from 0.74 to 0.97. The Best performing algorithm was Decision tree that achieved an F-measure of 0.97, 0.94 and 0.96 for classifying suicidal text into three levels of concern.
机译：社交网站（SNS）的巨大增长导致了过去十年中的人民更虚拟参与。通过这些SNS生成的数据量是巨大的，允许研究人员分析这一大数据。人们分享他们与任何兴趣主题相关的意见和思想。由于自杀是全世界死亡的主要原因，它已成为不同的研究人员正在工作的热门话题。由于社会隔离，Covid19进一步扩大了危机，这是自杀的主要危险因素。通常通过使用调查问卷和面部面对面设置来分析和处理问题，并通过生理观点来处理，但社会耻辱导致其疗效。在我们的研究中，我们利用知名机器学习算法来为社交媒体进行多分类的自杀风险，以便可以妥善确定具有高风险的个人以挽救珍贵的人类生命。数据已经通过四种流行的机器学习算法进行了实验：Logistic回归，多项式Naïve贝叶斯，支持向量机和决策树。产生的结果令人印象深，F1分数范围为0.74至0.97。最佳执行算法是决策树，其实现了0.97,0.94和0.96的F-Measure，用于将自杀文本分为三个关注程度。

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