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Depression recognition based on emotional information fused with attentional mechanism
- Chinese Journal of Intelligent Science and Technology Vol. 4, Issue 4, Pages: 600-609(2022)
- 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：
- 基金信息：
DOI：10.11959/j.issn.2096-6652.202221
CLC： TP389.1, TP183, TP391.1-
Published Online：2022-12，
Published：15 December 2022
- 稿件说明：
移动端阅览
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.
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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