面向微博文本的自杀风险识别模型

Suicide Risk Identification Model Based on Microblog Text

自杀是当今社会严重的公共卫生问题,对自杀预防工作进行深入研究有着极大的社会意义.该文对基于微博文本的自杀风险评估方法进行了研究.针对微博文本的特点,为解决当前神经网络单一结构在预测精度提升上的瓶颈问题,本文提出了一种混合架构的神经网络模型nC-BiLSTM,并将其应用于微博文本自杀风险识别.该模型利用多路不同卷积核的卷积层提取局部特征信息,同时使用双向长短期记忆网络层提取句子的上下文语义特征信息,实验表明nC-BiLSTM模型的识别精准率、召回率、F值均优于其它模型.该研究成果可应用到自杀预防的早期干预中.

Suicide is a serious public health problem in today’s society.It is of great social significance to conduct indepth research on suicide prevention.This work studies the suicide risk assessment method based on Microblog text.According to Microblog text features,in order to solve the bottleneck problem of the current neural network single structure in the prediction accuracy improvement,this study proposes a hybrid architecture neural network model nCBiLSTM and applies it to the Microblog text suicide risk identification.The model extracts local feature information by using multiple convolutional layers of different convolution kernels,and extracts contextual semantic feature information of sentences by using Bidirectional Long Short-Term Memory(BiLSTM)network layer.The experimental results show that the recognition accuracy,recall rate,and F value of the nC-BiLSTM model are better than other models.The results of this study can be applied to the early intervention of suicide prevention.