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
付淇 1张丽园 2戴欢3
作者信息
- 1. 江西科技师范大学信息与机电工程学院,江西 南昌 330036
- 2. 豫章师范学院数学与计算机学院,江西 南昌 330103
- 3. 江西省科技基础条件平台中心,江西 南昌 330003
- 折叠
摘要
自杀是世界上最重大的公共卫生危机之一,它已超过战争、他杀和自然灾害加在一起的死亡总和.本文在具有自杀意念的社交媒体的文本中使用计算机技术、机器学习和深度学习的方法,自动抽取自杀意念原因,并探索内容特征(词、词性、语法)和情感心理特征(语言、情感、自杀心理)对自杀意念原因自动抽取任务的影响.实验结果表明,内容特征作为特征中最主要和最重要的特征表现较好,其中词特征的表现最好,而词性特征和语法特征由于词本身的包含关系,在某种程度上被词特征所覆盖.情感心理特征则对内容特征有较好的完善和补充的效果,情感、情绪或心理的表达对自杀意念原因有较相关的正比例关系.
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引用本文复制引用
基金项目
江西省教育厅科技项目(GJJ2201339)
江西省教育厅科技项目(GJJ191220)
江西科技师范大学校级博士科研启动基金资助项目(2022BSQD38)
江西省高校人文社科项目(TQ23101)
江西省高校人文社科项目(TQ19203)
江西省自然科学基金管理科学类项目(20213BAA10W03)
出版年
2024
国家科技期刊平台
NSTL
万方数据