Chinese Journal of Intelligent Science and Technology ›› 2022, Vol. 4 ›› Issue (4): 600-609.doi: 10.11959/j.issn.2096-6652.202221

• Papers and Reports • Previous Articles     Next Articles

Depression recognition based on emotional information fused with attentional mechanism

Yan CHEN1, Xueqin LUO2, Wei LIANG1, Yongfang XIE3   

  1. 1 School of Advanced Interdisciplinary Studies, Hunan University of Technology and Business, Changsha 410205, China
    2 School of Computer Science, Hunan University of Technology and Business, Changsha 410205, China
    3 School of Automation, Central South University, Changsha 410083, China
  • Revised:2021-12-17 Online:2022-12-15 Published:2022-12-01
  • Supported by:
    The National Natural Science Foundation of China(71971080);The National Natural Science Foundation of China(71601077);The Natural Science Foundation of Hunan Province(2020JJ4252)


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: 

No Suggested Reading articles found!