吉林大学学报(工学版) ›› 2024, Vol. 54 ›› Issue (4): 1078-1085.doi: 10.13229/j.cnki.jdxbgxb.20220704

• 计算机科学与技术 • 上一篇    

基于异质图网络的心理评估方法

金志刚1(),苏仁鋆1,赵晓芳2   

  1. 1.天津大学 电气自动化与信息工程学院,天津 300072
    2.天津城建大学 计算机与信息工程学院,天津 300384
  • 收稿日期:2022-06-06 出版日期:2024-04-01 发布日期:2024-05-17
  • 作者简介:金志刚(1972-),男,教授,博士.研究方向:社交网络与大数据,水下网络,传感器网络,网络安全. E-mail: zgjin@tju.edu.cn
  • 基金资助:
    国家自然科学基金项目(71502125)

Psychological assessment method based on heterogeneous graph network

Zhi-gang JIN1(),Ren-jun SU1,Xiao-fang ZHAO2   

  1. 1.School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China
    2.School of Computer and Information Engineering,Tianjin Chengjian University,Tianjin 300384,China
  • Received:2022-06-06 Online:2024-04-01 Published:2024-05-17

摘要:

针对现有利用社交媒体数据通过计算机技术进行心理评估的方法存在语义稀疏和缺乏先验知识融入的问题,提出了基于异质图网络的心理评估方法,提取社交媒体上的用户画像信息、文本信息、主题信息,构建异质图,将心理评估问题描述为节点分类问题。一方面,构建用户心理状态知识图谱融入先验知识,表征用户画像信息;另一方面,利用异质图融合信息进行语义补充,通过分层注意力关注节点权重,得到评估结果。在Twitter和Reddit数据集上进行实验,结果证明了本文方法在用户心理状态分类上的有效性,且关键性能明显提升。

关键词: 人工智能, 异质图网络, 心理评估, 社交媒体, 知识图谱

Abstract:

In view of the problems of semantic sparseness and lack of integration of prior knowledge in the existing methods of psychological assessment using social media data through computer technology, a psychological assessment method based on heterogeneous graph network is proposed. A heterogeneous graph is constructed by extracting users' profile information, text information, and topic information on social media, and the psychological assessment problem is described as a node classification problem. On the one hand, building a knowledge graph of social media users' mental state aims to integrate prior knowledge and represent the users' profile information; On the other hand, constructing a heterogeneous graph to integrate heterogeneous information and make semantic complements, and the hierarchical attention mechanism is used to pay attention to the node weights to obtain the evaluation results. Experiments are carried out on the Twitter and Reddit dataset, and the results show that the model is effective in classifying users' mental states, and the key performance is significantly improved.

Key words: artificial intelligence, heterogeneous graph network, psychological assessment, social media, knowledge graph

中图分类号: 

  • TP183

图1

方法流程示意图及异质图模型结构"

图2

用户心理画像知识图谱"

表1

身体信息、既往经历、人格因素相关词汇示例"

属性相关词
身体信息unenergetic, indifferent, lackadaisical, dull, tired, bored, empty, overworked, drowsy, lethargic, sluggish, slow-moving
既往经历Amitriptyline, Fluoxetine, Sertraline, seppuku, suicide, self-immolation, self-slaughter,
人格因素perfect, perfectionist, not enough, fat, bad, weak, tired, unconfident, afraid, anxious

表2

Twitter数据集示例"

抑郁用户标志文本抑郁用户文本非抑郁用户文本
I've been diagnosed with Generalized Anxiety Disorder and Clinical Depression.Tbh if I died my parents would probably yell at me.While i miss having cool nice cars that go fast it's nice paying like 250 per service.
I only caught the depression one...I have been diagnosed for about 5 years…I've forgotten how to breathe.It was Snowing and now it's raining, I prefer the snow!

表3

Reddit数据集示例"

标签Not DepressedModerately DepressedSeverely Depressed
示例Happy New year everyone, meditate today. Hope all is well.Sat in the dark and cried myself going into the new year.I cut myself today: I cut myself while in the shower today.
训练集1 9716 019901
开发集1 8302 306360

表4

Twitter数据集对比实验结果 (%)"

方法AccuracyF1PrecisionRecall
MDL84.884.984.885.0
MSNL81.881.881.881.8
WDL76.876.876.976.8
NB72.472.872.772.8
GRU82.482.482.582.3
GRU+RS76.075.676.076.8
BERT84.4584.4084.5084.40
RoBERTa84.9584.7084.6084.90
DANs84.884.884.884.8
Modality Attention86.686.486.886.2
HGATKG87.887.687.787.5

表5

Reddit数据集对比实验结果"

方法AccuracyF1PrecisionRecall
BERTlarge60.3056.6056.8056.60
RoBERTalarge66.4060.5062.9059.10
HGATKG75.2563.9075.4059.20

图3

消融实验"

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