计算机与现代化Issue(4)：77-82,6.DOI:10.3969/j.issn.1006-2475.2024.04.013
基于机器学习的自杀意念原因特征分析
Features Analysis of Suicide Ideation Causes Based on Machine Learning
摘要
Abstract
Suicide is one of the most significant public health crises globally,surpassing the combined mortality rate of wars,ho-micides,and natural disasters.This study employs computer technology,machine learning,and deep learning methods to ana-lyze social media texts that contain suicidal ideation,aiming to automatically extract the underlying causes of suicidal thoughts.The study investigates the impact of content features(such as words,parts of speech,dependency syntactic parsing)and emotional-psychological features(including linguistics,emotions,suicidal psychology)on the task of automatically extracting causes of suicidal ideation.Experimental results indicate that content features perform notably well and are the most significant and crucial factors among the features.Specifically,word features exhibit the best performance,while parts of speech and depen-dency syntactic parsing features are overshadowed by the inclusion of word features to some extent.In contrast,emotional-psychological features effectively complement and enhance content features.The expression of emotions,sentiments,or psycho-logical aspects shows a positive correlation with the underlying causes of suicidal ideation.关键词
自杀意念/自杀意念原因/社交文本/CRF/特征Key words
suicide ideation/suicide ideation causes/social text/CRF/feature分类
信息技术与安全科学引用本文复制引用
付淇,张丽园,戴欢..基于机器学习的自杀意念原因特征分析[J].计算机与现代化,2024,(4):77-82,6.基金项目
江西省教育厅科技项目(GJJ2201339,GJJ191220) （GJJ2201339,GJJ191220）
江西科技师范大学校级博士科研启动基金资助项目(2022BSQD38) （2022BSQD38）
江西省高校人文社科项目(TQ23101,TQ19203) （TQ23101,TQ19203）
江西省自然科学基金管理科学类项目(20213BAA10W03) （20213BAA10W03）
