现代电子技术2024，Vol.47Issue(14)：9-14,6.DOI:10.16652/j.issn.1004-373x.2024.14.002
改进CapsNet的文本自杀风险检测模型
Improved CapsNet text suicide risk detection model
摘要
Abstract
In allusion to the problem that the existing models do not make full use of the historical dynamic information of text in social media for suicide risk detection,the CapsNet model is introduced.In the CapsNet model,groups of directed neurons are transmitted between layers,which can better perceive spatial information in long texts,find emotional trends of social media users,and provide a basis for suicide risk detection.The CapsNet model is improved.The scale space is changed and the network width is increased to fully extract the feature information hidden in the sentence.The exponential function is used to optimize the Squash function,so as to enlarge the capsule output,and make full use of the capsule to extract the feature information in user's historical dynamics.In dynamic routing,an optimization algorithm is used to initialize the coupling coefficient to remove the interference of noisy capsules.The pre-trained SBERT model is used to extract features of social media text data.The binary classification accuracy of the improved CapsNet text suicide risk detection model can reach 95.93%,and the F1 score can reach 95.86%,which is better than other models of suicide risk detection.关键词
CapsNet模型/自杀风险检测/社交媒体/长文本信息/特征提取/SBERT模型Key words
CapsNet model/suicide risk detection/social media/long text information/feature exteraction/SBERT model分类
电子信息工程引用本文复制引用
陈彬,李鸿燕,梁卓..改进CapsNet的文本自杀风险检测模型[J].现代电子技术,2024,47(14):9-14,6.基金项目
国家自然科学基金项目(62201377) （62201377）
山西省回国留学人员科研资助项目(2022-072) （2022-072）
