The suicidal death rate is growing rapidly. Depression and stress levels among the people have increased significantly, which is considered to be a risk factor for suicidal thoughts. Social media is gradually more popular and people use them for sharing their sentiments and harmful emotions related to suicidal thoughts. An effective approach is required to investigate for identifying risk factors associated with suicide on social media. The objective is to propose some learning models to evaluate social media data to identify persons having suicidal tendencies. A large data consisting of 8452 tweets are collected from Twitter, pre-processed and bags of words were applied. Different machine learning and deep learning algorithms such as Random Forest, Decision Tree, Bernoulli Naïve Bayes, Multinomial Naïve Bayes, Recurrent Neural Network, Artificial Neural Network and Long Short Term Memory were applied for classifying the tweets in two sets: suicidal and non-suicidal. The performance of these learning models is further evaluated on three parameters: accuracy, precision and recall. These models have shown significant results on the parameters.

中文翻译：

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识别自杀念头的学习模型的性能评估

自杀死亡率正在迅速增长。人们的抑郁和压力水平显着增加，这被认为是自杀念头的危险因素。社交媒体逐渐流行起来，人们用它们来分享与自杀念头有关的情绪和有害情绪。需要一种有效的方法来调查在社交媒体上识别与自杀相关的风险因素。目的是提出一些学习模型来评估社交媒体数据，以识别有自杀倾向的人。从 Twitter 收集了由 8452 条推文组成的大数据，经过预处理并应用了词袋。不同的机器学习和深度学习算法，例如随机森林、决策树、伯努利朴素贝叶斯、多项朴素贝叶斯、递归神经网络、应用人工神经网络和长期短期记忆将推文分为两组：自杀性和非自杀性。这些学习模型的性能进一步评估三个参数：准确度、精确度和召回率。这些模型在参数上显示出显着的结果。