基于情感信息融合注意力机制的抑郁症识别

陈妍; 罗雪琴; 梁伟; 谢永芳

doi:10.11959/j.issn.2096-6652.202221

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基于情感信息融合注意力机制的抑郁症识别

学术论文 | 更新时间：2024-06-05

- 基于情感信息融合注意力机制的抑郁症识别
- Depression recognition based on emotional information fused with attentional mechanism
- 智能科学与技术学报 2022年4卷第4期页码：600-609
- 作者机构：
  
  1. 湖南工商大学前沿交叉学院，湖南长沙 410205
  2. 湖南工商大学计算机学院，湖南长沙 410205
  3. 中南大学自动化学院，湖南长沙 410083
- 作者简介：
  
  [ "陈妍（1981− ），女，博士，湖南工商大学前沿交叉学院副教授，主要研究方向为大数据分析与智能决策、深度学习、智慧医疗" ]
  [ "罗雪琴（1996− ），女，湖南工商大学计算机学院硕士生，主要研究方向为自然语言处理、深度学习、智慧医疗" ]
  [ "梁伟（1982− ），男，博士，湖南工商大学前沿交叉学院副教授，主要研究方向为自然语言处理、大数据分析、深度学习、智慧医疗" ]
  [ "谢永芳（1972− ），男，博士，中南大学自动化学院教授、博士生导师，主要研究方向为深度学习、智能优化、智能控制、知识自动化" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(71971080);国家自然科学基金资助项目;The National Natural Science Foundation of China(71601077);湖南省自然科学基金资助项目;The Natural Science Foundation of Hunan Province(2020JJ4252)
- DOI：10.11959/j.issn.2096-6652.202221
  中图分类号： TP389.1, TP183, TP391.1
- 网络出版日期：2022-12，
  
  纸质出版日期：2022-12-15
- 稿件说明：
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陈妍, 罗雪琴, 梁伟, 等. 基于情感信息融合注意力机制的抑郁症识别[J]. 智能科学与技术学报, 2022,4(4):600-609.

Yan CHEN, Xueqin LUO, Wei LIANG, et al. Depression recognition based on emotional information fused with attentional mechanism[J]. Chinese journal of intelligent science and technology, 2022, 4(4): 600-609.
陈妍, 罗雪琴, 梁伟, 等. 基于情感信息融合注意力机制的抑郁症识别[J]. 智能科学与技术学报, 2022,4(4):600-609. DOI： 10.11959/j.issn.2096-6652.202221.

Yan CHEN, Xueqin LUO, Wei LIANG, et al. Depression recognition based on emotional information fused with attentional mechanism[J]. Chinese journal of intelligent science and technology, 2022, 4(4): 600-609. DOI： 10.11959/j.issn.2096-6652.202221.

摘要

当前利用社交媒体数据预测抑郁症的研究忽视语言风格和情感随时间推移变化的特征，缺乏对情感状态和帖子元数据特征的研究，基于此，提出一种基于情感信息融合注意力机制的抑郁症识别模型。首先，在现有抑郁症识别研究的基础上，使用文本分类卷积神经网络按时间顺序提取用户每个时间段的帖子信息和情感信息，引入注意力机制为得到的特征矩阵分配不同的注意力权重，进而得到用户帖子信息特征和用户情感信息特征。其次，使用正则匹配提取文本的情感倾向信息，拼接注意力机制输出分配权重后的情感特征矩阵，以增强情感学习表示。然后，加入描述社交网络帖子的元数据特征，设计用于表示用户偏好特征的指标，通过统计表示指标提取用户语言偏好特征。最后，融合用户语言信息、用户情感信息、用户语言偏好信息3种特征，建立基于多层感知机的用户抑郁状态识别模型。实验结果显示，提出的模型精确率提高了0.051，召回率提高了0.065，F1值提高了0.058。

Abstract

Aiming at the current research on using social media data to predict depression ignoring the characteristics of language style and emotional changes over time and lacking of research on the characteristics of emotional state and post metadata

a depression recognition model based on emotional information fused with attentional mechanism was proposed.Firstly

on the basis of the existing research on depression recognition

the text classification convolutional neural network was used to extract the post information and emotional information for each time period of the user in chronological order

and the attention mechanism was introduced to assign different attention weights to the obtained feature matrix to obtain user post information features and user emotional information features.Next

regular matching was used to extract the emotional tendency information

and the weighted emotional feature matrix output by the attention mechanism was spliced to enhance emotional learning expression.Then

metadata features describing social network posts were added

indicators that characterized user preference features were designed

and user language preference features through statistical characterization indicators were extracted.Finally

the three characteristics of user language information

user emotional information

and user language preference information were combined to establish a prediction model for the user’s depression state based on a multi-layer perceptron.The experimental results showed that the accuracy of the model in this paper had increased by 0.051

the recall rate had increased by 0.065

and F1 value had increased by 0.058.

关键词

Keywords

references

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