基于情感信息融合注意力机制的抑郁症识别

doi:10.11959/j.issn.2096-6652.202221

Abstract

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.

Key words: depression recognition, attention mechanism, emotional information, deep learning

CLC Number:

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.

Figures/Tables 9

References 32

[1]	雍那, 杜莲, 胡华 ,等. 抑郁症患者攻击行为与应对方式、焦虑、抑郁的关系[J]. 第三军医大学学报, 2014,36(11): 1133-1137.
	YONG N , DU L , HU H ,et al. Correlation of aggressive behavior with coping styles,anxiety and depression in major depressed patients[J]. Journal of Third Military Medical University, 2014,36(11): 1133-1137.
[2]	CRUMP C , SUNDQUIST K , WINKLEBY M A ,et al. Mental disorders and vulnerability to homicidal death:Swedish nationwide cohort study[J]. BMJ (Clinical Research Ed), 2013,346:f557.
[3]	PARK M , CHA C , CHA M . Depressive moods of users portrayed in Twitter[J]. Proceedings of the ACM SIGKDD Workshop on Healthcare Informatics (HI-KDD), 2012: 1-8.
[4]	PARK M , MCDONALD D W , CHA M . Perception differences between the depressed and non-depressed users in twitter[C]// Proceedings of the 7th International AAAI Conference on Weblogs and Social Media.[S.l.:s.n.], 2013: 476-485.
[5]	XU R H , ZHANG Q P . Understanding online health groups for depression:social network and linguistic perspectives[J]. Journal of Medical Internet Research, 2016,18(3): e63.
[6]	DE CHOUDHURY M , GAMON M , COUNTS S ,et al. Predicting depression via social media[C]// Proceedings of the 7th International AAAI Conference on Weblogs and Social Media.[S.l.:s.n.], 2013: 128-137.
[7]	SHEN G Y , JIA J , NIE L Q ,et al. Depression detection via harvesting social media:a multimodal dictionary learning solution[C]// Proceedings of the 26th International Joint Conference on Artificial Intelligence. California:International Joint Conferences on Artificial Intelligence Organization, 2017: 3838-3844.
[8]	TROTZEK M , KOITKA S , FRIEDRICH C M . Utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences[J]. IEEE Transactions on Knowledge and Data Engineering, 2018,32(3): 588-601.
[9]	KIM Y , . Convolutional neural networks for sentence classification[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg:Association for Computational Linguistics, 2014.
[10]	LIU Y , WU Y F . FNED:a deep network for fake news early detection on social media[J]. ACM Transactions on Information Systems, 2020,38(3): 1-33.
[11]	NASKAR D , SINGH S R , KUMAR D ,et al. Emotion dynamics of public opinions on twitter[J]. ACM Transactions on Information Systems, 2020,38(2): 1-24.
[12]	叶俊民, 罗达雄, 陈曙 . 基于短文本情感增强的在线学习者成绩预测方法[J]. 自动化学报, 2020,46(9): 1927-1940.
	YE J M , LUO D X , CHEN S . Short-text sentiment enhanced achievement prediction method for online learners[J]. Acta Automatica Sinica, 2020,46(9): 1927-1940.
[13]	BAHDANAU D , CHO K , BENGIO Y . Neural machine translation by jointly learning to align and translate[J]. arXiv preprint, 2014,arXiv:1409.0473.
[14]	沈继锋, 刘岳, 韦浩 ,等. 基于差分特征注意力机制的无锚框多光谱行人检测算法[J]. 智能科学与技术学报, 2021,3(3): 294-303.
	SHEN J F , LIU Y , WEI H ,et al. Anchor free multispectral pedestrian detection algorithm based on differential feature attention mechanism[J]. Chinese Journal of Intelligent Science and Technology, 2021,3(3): 294-303.
[15]	洪依, 孙成立, 冷严 . 基于超轻量通道注意力的端对端语音增强方法[J]. 智能科学与技术学报, 2021,3(3): 351-358.
	HONG Y , SUN C L , LENG Y . End-to-end speech enhancement based on ultra-lightweight channel attention[J]. Chinese Journal of Intelligent Science and Technology, 2021,3(3): 351-358.
[16]	宋婷, 陈战伟, 杨海峰 . 基于分层注意力网络的方面情感分析[J]. 大数据, 2020,6(5): 82-91.
	SONG T , CHEN Z W , YANG H F . Aspect sentiment analysis based on a hierarchical attention network[J]. Big Data Research, 2020,6(5): 82-91.
[17]	PENNINGTON J , SOCHER R , MANNING C . Glove:global vectors for word representation[C]// Proceedings of 2014 Conference on Empirical Methods in Natural Language Processing. Stroudsburg:Association for Computational Linguistics, 2014: 1532-1543.
[18]	VASWANI A , SHAZEER N , PARMAR N ,et al. Attention is all you need[J]. arXiv preprint, 2017,arXiv:1706.03762v5.
[19]	曾淦雄, 柯逍 . 基于 3D 卷积的图像序列特征提取与自注意力的车牌识别方法[J]. 智能科学与技术学报, 2021,3(3): 268-279.
	ZENG G X , KE X . 3D convolution-based image sequence feature extraction and self-attention for license plate recognition method[J]. Chinese Journal of Intelligent Science and Technology, 2021,3(3): 268-279.
[20]	HUTTO C , GILBERT E . VADER:a parsimonious rule-based model for sentiment analysis of social media text[C]// Proceedings of the 8th International AAAI Conference on Web and Social Media.[S.l.:s.n.], 2014.
[21]	LEIS A , RONZANO F , MAYER M A ,et al. Detecting signs of depression in tweets in Spanish:behavioral and linguistic analysis[J]. Journal of Medical Internet Research, 2019,21(6): e14199.
[22]	HUSSAIN J , SATTI F A , AFZAL M ,et al. Exploring the dominant features of social media for depression detection[J]. Journal of Information Science, 2020,46(6): 739-759.
[23]	CAVAZOS-REHG P A , KRAUSS M J , SOWLES S ,et al. A content analysis of depression-related Tweets[J]. Computers in Human Behavior, 2016,54: 351-357.
[24]	BENTON A , COPPERSMITH G , DREDZE M . Ethical research protocols for social media health research[C]// Proceedings of the 1st ACL Workshop on Ethics in Natural Language Processing. Stroudsburg:Association for Computational Linguistics, 2017: 94-102.
[25]	COPPERSMITH G , DREDZE M , HARMAN C . Quantifying mental health signals in twitter[C]// Proceedings of the Workshop on Computational Linguistics and Clinical Psychology:From Linguistic Signal to Clinical Reality. Stroudsburg:Association for Computational Linguistics, 2014: 51-60.
[26]	ZEILER M D . ADADELTA:an adaptive learning rate method[J]. arXiv preprint, 2012,arXiv:1212.5701.
[27]	SRIVASTAVA N , HINTON G E , KRIZHEVSKY A ,et al. Dropout:a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014,15(1): 1929-1958.
[28]	PEDREGOSA F , VAROQUAUX G , GRAMFORT A ,et al. Scikit-learn:machine learning in Python[J]. Journal of Machine Learning Research, 2012,12(10): 2825-2830.
[29]	SONG X M , NIE L Q , ZHANG L M ,et al. Multiple social network learning and its application in volunteerism tendency prediction[C]// Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval.[S.l.:s.n.], 2015: 213-222.
[30]	ROLET A , CUTURI M , PEYRé G , . Fast dictionary learning with a smoothed Wasserstein loss[C]// Proceedings of the 18th Artificial Intelligence and Statistics.[S.l.:s.n.], 2016: 630-638.
[31]	CHUNG J , GULCEHRE C , CHO K ,et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[J]. arXiv preprint, 2014,arXiv:1412.3555.
[32]	LIU N , LU P , ZHANG W ,et al. Knowledge-aware deep dual networks for text-based mortality prediction[C]// Proceedings of 2019 IEEE 35th International Conference on Data Engineering. Piscataway:IEEE Press, 2019: 1406-1417.

Metrics

Recommended 0

No Suggested Reading articles found!

数据集	用户数/个	帖子数/条
Depressed	1 402	209 488
Non-Depressed	2 515	448 038

参数	值
lr	0.003
batchsize	20
hiddens	100
weight_decay	0.01
embed_size	200

预训练语料	精确率	召回率	F1值
Glove.200d	0.867	0.888	0.877
Glove.twitter.25d	0.845	0.847	0.846
Glove.twitter.50d	0.856	0.881	0.868
Glove.twitter.100d	0.859	0.895	0.877
Glove.twitter.200d	0.918	0.934	0.926

模型	精确率	召回率	F1值
NB	0.727	0.728	0.728
MSNL	0.818	0.818	0.818
WDL	0.769	0.768	0.768
MDL	0.848	0.850	0.849
GRU	0.825	0.823	0.824
TextCNN	0.825	0.852	0.838
BK-DDN	0.867	0.869	0.868
EIAM-DR	0.918	0.934	0.926

模型	精确率	召回率	F1值
EIAM-DR_(-sep)	0.853	0.875	0.864
EIAM-DR_(-ep)	0.869	0.904	0.886
EIAM-DR_(-p)	0.898	0.933	0.915
EIAM-DR	0.918	0.934	0.926

Depression recognition based on emotional information fused with attentional mechanism

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 32

Related Articles 15

Metrics

Recommended 0

[1]	Zhe HUANG, Yongcai WANG, Deying LI. A survey of 3D object detection algorithms [J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(1): 7-31.
[2]	Jingwei LU, Xiang CHENG, Fei-Yue WANG. Artificial intelligence and deep learning methods for solving differential equations: the state of the art and prospects [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(4): 461-476.
[3]	Jun ZHANG, Peidong XU, Siyuan CHEN, Tianlu GAO, Yuxin DAI, Ke ZHANG, Hang ZHAO, Jiemai GAO, Yuyang BAI, Jinxing LI, Haoran ZHANG, Xiang LI, Jiuxiang CHEN. A hybrid physics-data-knowledge driven approach for human-machine hybrid-augmented intelligence-based system management and control [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(4): 571-583.
[4]	Renwu LI, Lingxiao ZHANG, Lin GAO, Chunpeng LI, Hao JIANG. Category-level object pose estimation from depth point cloud [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(2): 246-254.
[5]	Linhui LI, Bin ZHOU, Weiwei REN, Jing LIAN. Review of pedestrian trajectory prediction methods [J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(4): 399-411.
[6]	Jifeng SHEN, Yue1 LIU, Hao WEI, Xin ZUO, Wankou YANG. Anchor free multispectral pedestrian detection algorithm based on differential feature attention mechanism [J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(3): 294-303.
[7]	Ying LI, Long CHEN, Zhaohong HUANG, Yang SUN, Guorong CAI. Plant leaf detection technology based on multi-scale CNN feature fusion [J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(3): 304-311.
[8]	Qing TIAN, Rong HU, Zuoyong LI, Yuanzheng CAI, Zhaochai YU. Insulator detection based on SE-YOLOv5s [J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(3): 312-321.
[9]	Kaishi YIN, Meng YANG, Xi GU, Zhicheng WANG. Light weight rotating object detector based on angle sensitive spatial attention mechanism [J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(3): 322-333.
[10]	Wen LIU, Kunlin HU, Yan LI, Zhao LIU. A review of prediction methods for moving target trajectories [J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(2): 149-160.
[11]	Yang ZHANG, Yue HU, Dongrong XIN. A deep learning short-term traffic flow prediction method considering spatial-temporal association [J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(2): 172-178.
[12]	Dongjun LIU, Yuhan WANG, Wenfen LING, Yong PENG, Wanzeng KONG. Emotion recognition based on brain and machine collaborative intelligence [J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(1): 65-75.
[13]	Liang ZHAO, Zhifeng XIE, Kunpeng ZHANG, Yuqing ZHENG, Yuankun FU. Modeling signal propagation in wireless network:an interval type-2 fuzzy ensemble deep learning approach [J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(4): 401-411.
[14]	Sijia TIAN,Qiang GU,Rong HU,Ruige LI,Dingxin HE. A robot sorting method based on deep learning [J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(3): 268-274.
[15]	Fei-Yue WANG,Dongpu CAO,Qinglai WEI. Reinforcement learning:toward action-knowledge merged intelligent mechanisms and algorithms [J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(2): 101-106.